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parulnith/Music_genre_classification.ipynb

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Music_genre_classification.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Untitled9.ipynb",
"version": "0.3.2",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "TPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/parulnith/7f8c174e6ac099e86f0495d3d9a4c01e/untitled9.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "cNnM2w-HCeb1",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Music genre classification notebook"
]
},
{
"metadata": {
"id": "2l3sppZMCydR",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Importing Libraries"
]
},
{
"metadata": {
"id": "Gt3fyg6dCNvX",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# feature extractoring and preprocessing data\n",
"import librosa\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import os\n",
"from PIL import Image\n",
"import pathlib\n",
"import csv\n",
"\n",
"# Preprocessing\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
"\n",
"#Keras\n",
"import keras\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "DPe_ebYuDqr5",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Extracting music and features\n",
"\n",
"### Dataset\n",
"\n",
"We use [GTZAN genre collection](http://marsyasweb.appspot.com/download/data_sets/) dataset for classification. \n",
"<br>\n",
"<br>\n",
"The dataset consists of 10 genres i.e\n",
" * Blues\n",
" * Classical\n",
" * Country\n",
" * Disco\n",
" * Hiphop\n",
" * Jazz\n",
" * Metal\n",
" * Pop\n",
" * Reggae\n",
" * Rock\n",
" \n",
"Each genre contains 100 songs. Total dataset: 1000 songs"
]
},
{
"metadata": {
"id": "neqMS0VoDpN5",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
""
]
},
{
"metadata": {
"id": "AfBSVfRCD3PE",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Extracting the Spectrogram for every Audio"
]
},
{
"metadata": {
"id": "BHh3pTEVDdrT",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"cmap = plt.get_cmap('inferno')\n",
"\n",
"plt.figure(figsize=(10,10))\n",
"genres = 'blues classical country disco hiphop jazz metal pop reggae rock'.split()\n",
"for g in genres:\n",
" pathlib.Path(f'img_data/{g}').mkdir(parents=True, exist_ok=True) \n",
" for filename in os.listdir(f'./MIR/genres/{g}'):\n",
" songname = f'./MIR/genres/{g}/{filename}'\n",
" y, sr = librosa.load(songname, mono=True, duration=5)\n",
" plt.specgram(y, NFFT=2048, Fs=2, Fc=0, noverlap=128, cmap=cmap, sides='default', mode='default', scale='dB');\n",
" plt.axis('off');\n",
" plt.savefig(f'img_data/{g}/{filename[:-3].replace(\".\", \"\")}.png')\n",
" plt.clf()\n",
" "
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "SszVgjYnFNX9",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"All the audio files get converted into their respective spectrograms .WE can noe easily extract features from them."
]
},
{
"metadata": {
"id": "3Nw9HpSdFRsW",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
""
]
},
{
"metadata": {
"id": "piwUwgP5Eef9",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Extracting features from Spectrogram\n",
"\n",
"\n",
"We will extract\n",
"\n",
"* Mel-frequency cepstral coefficients (MFCC)(20 in number)\n",
"* Spectral Centroid,\n",
"* Zero Crossing Rate\n",
"* Chroma Frequencies\n",
"* Spectral Roll-off."
]
},
{
"metadata": {
"id": "__g8tX8pDeIL",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'\n",
"for i in range(1, 21):\n",
" header += f' mfcc{i}'\n",
"header += ' label'\n",
"header = header.split()"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "TBlT448pEqR9",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Writing data to csv file\n",
"\n",
"We write the data to a csv file "
]
},
{
"metadata": {
"id": "ZsSQmB0PE3Iu",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"file = open('data.csv', 'w', newline='')\n",
"with file:\n",
" writer = csv.writer(file)\n",
" writer.writerow(header)\n",
"genres = 'blues classical country disco hiphop jazz metal pop reggae rock'.split()\n",
"for g in genres:\n",
" for filename in os.listdir(f'./MIR/genres/{g}'):\n",
" songname = f'./MIR/genres/{g}/{filename}'\n",
" y, sr = librosa.load(songname, mono=True, duration=30)\n",
" chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)\n",
" spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr)\n",
" spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)\n",
" rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)\n",
" zcr = librosa.feature.zero_crossing_rate(y)\n",
" mfcc = librosa.feature.mfcc(y=y, sr=sr)\n",
" to_append = f'{filename} {np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spec_cent)} {np.mean(spec_bw)} {np.mean(rolloff)} {np.mean(zcr)}' \n",
" for e in mfcc:\n",
" to_append += f' {np.mean(e)}'\n",
" to_append += f' {g}'\n",
" file = open('data.csv', 'a', newline='')\n",
" with file:\n",
" writer = csv.writer(file)\n",
" writer.writerow(to_append.split())"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "0yfdo1cj6V7d",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"The data has been extracted into a [data.csv](https://github.com/parulnith/Music-Genre-Classification-with-Python/blob/master/data.csv) file."
]
},
{
"metadata": {
"id": "fgeCZSKQEp1A",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Analysing the Data in Pandas"
]
},
{
"metadata": {
"id": "Kr5_EdpD9dyh",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 253
},
"outputId": "81fd4a29-93fa-44f8-bf90-2f99981f761a"
},
"cell_type": "code",
"source": [
"data = pd.read_csv('data.csv')\n",
"data.head()"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" <th>chroma_stft</th>\n",
" <th>rmse</th>\n",
" <th>spectral_centroid</th>\n",
" <th>spectral_bandwidth</th>\n",
" <th>rolloff</th>\n",
" <th>zero_crossing_rate</th>\n",
" <th>mfcc1</th>\n",
" <th>mfcc2</th>\n",
" <th>mfcc3</th>\n",
" <th>...</th>\n",
" <th>mfcc12</th>\n",
" <th>mfcc13</th>\n",
" <th>mfcc14</th>\n",
" <th>mfcc15</th>\n",
" <th>mfcc16</th>\n",
" <th>mfcc17</th>\n",
" <th>mfcc18</th>\n",
" <th>mfcc19</th>\n",
" <th>mfcc20</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>blues.00081.au</td>\n",
" <td>0.380260</td>\n",
" <td>0.248262</td>\n",
" <td>2116.942959</td>\n",
" <td>1956.611056</td>\n",
" <td>4196.107960</td>\n",
" <td>0.127272</td>\n",
" <td>-26.929785</td>\n",
" <td>107.334008</td>\n",
" <td>-46.809993</td>\n",
" <td>...</td>\n",
" <td>14.336612</td>\n",
" <td>-13.821769</td>\n",
" <td>7.562789</td>\n",
" <td>-6.181372</td>\n",
" <td>0.330165</td>\n",
" <td>-6.829571</td>\n",
" <td>0.965922</td>\n",
" <td>-7.570825</td>\n",
" <td>2.918987</td>\n",
" <td>blues</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>blues.00022.au</td>\n",
" <td>0.306451</td>\n",
" <td>0.113475</td>\n",
" <td>1156.070496</td>\n",
" <td>1497.668176</td>\n",
" <td>2170.053545</td>\n",
" <td>0.058613</td>\n",
" <td>-233.860772</td>\n",
" <td>136.170239</td>\n",
" <td>3.289490</td>\n",
" <td>...</td>\n",
" <td>-2.250578</td>\n",
" <td>3.959198</td>\n",
" <td>5.322555</td>\n",
" <td>0.812028</td>\n",
" <td>-1.107202</td>\n",
" <td>-4.556555</td>\n",
" <td>-2.436490</td>\n",
" <td>3.316913</td>\n",
" <td>-0.608485</td>\n",
" <td>blues</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>blues.00031.au</td>\n",
" <td>0.253487</td>\n",
" <td>0.151571</td>\n",
" <td>1331.073970</td>\n",
" <td>1973.643437</td>\n",
" <td>2900.174130</td>\n",
" <td>0.042967</td>\n",
" <td>-221.802549</td>\n",
" <td>110.843071</td>\n",
" <td>18.620984</td>\n",
" <td>...</td>\n",
" <td>-13.037723</td>\n",
" <td>-12.652228</td>\n",
" <td>-1.821905</td>\n",
" <td>-7.260097</td>\n",
" <td>-6.660252</td>\n",
" <td>-14.682694</td>\n",
" <td>-11.719264</td>\n",
" <td>-11.025216</td>\n",
" <td>-13.387260</td>\n",
" <td>blues</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>blues.00012.au</td>\n",
" <td>0.269320</td>\n",
" <td>0.119072</td>\n",
" <td>1361.045467</td>\n",
" <td>1567.804596</td>\n",
" <td>2739.625101</td>\n",
" <td>0.069124</td>\n",
" <td>-207.208080</td>\n",
" <td>132.799175</td>\n",
" <td>-15.438986</td>\n",
" <td>...</td>\n",
" <td>-0.613248</td>\n",
" <td>0.384877</td>\n",
" <td>2.605128</td>\n",
" <td>-5.188924</td>\n",
" <td>-9.527455</td>\n",
" <td>-9.244394</td>\n",
" <td>-2.848274</td>\n",
" <td>-1.418707</td>\n",
" <td>-5.932607</td>\n",
" <td>blues</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>blues.00056.au</td>\n",
" <td>0.391059</td>\n",
" <td>0.137728</td>\n",
" <td>1811.076084</td>\n",
" <td>2052.332563</td>\n",
" <td>3927.809582</td>\n",
" <td>0.075480</td>\n",
" <td>-145.434568</td>\n",
" <td>102.829023</td>\n",
" <td>-12.517677</td>\n",
" <td>...</td>\n",
" <td>7.457218</td>\n",
" <td>-10.470444</td>\n",
" <td>-2.360483</td>\n",
" <td>-6.783624</td>\n",
" <td>2.671134</td>\n",
" <td>-4.760879</td>\n",
" <td>-0.949005</td>\n",
" <td>0.024832</td>\n",
" <td>-2.005315</td>\n",
" <td>blues</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" filename chroma_stft rmse spectral_centroid \\\n",
"0 blues.00081.au 0.380260 0.248262 2116.942959 \n",
"1 blues.00022.au 0.306451 0.113475 1156.070496 \n",
"2 blues.00031.au 0.253487 0.151571 1331.073970 \n",
"3 blues.00012.au 0.269320 0.119072 1361.045467 \n",
"4 blues.00056.au 0.391059 0.137728 1811.076084 \n",
"\n",
" spectral_bandwidth rolloff zero_crossing_rate mfcc1 \\\n",
"0 1956.611056 4196.107960 0.127272 -26.929785 \n",
"1 1497.668176 2170.053545 0.058613 -233.860772 \n",
"2 1973.643437 2900.174130 0.042967 -221.802549 \n",
"3 1567.804596 2739.625101 0.069124 -207.208080 \n",
"4 2052.332563 3927.809582 0.075480 -145.434568 \n",
"\n",
" mfcc2 mfcc3 ... mfcc12 mfcc13 mfcc14 mfcc15 \\\n",
"0 107.334008 -46.809993 ... 14.336612 -13.821769 7.562789 -6.181372 \n",
"1 136.170239 3.289490 ... -2.250578 3.959198 5.322555 0.812028 \n",
"2 110.843071 18.620984 ... -13.037723 -12.652228 -1.821905 -7.260097 \n",
"3 132.799175 -15.438986 ... -0.613248 0.384877 2.605128 -5.188924 \n",
"4 102.829023 -12.517677 ... 7.457218 -10.470444 -2.360483 -6.783624 \n",
"\n",
" mfcc16 mfcc17 mfcc18 mfcc19 mfcc20 label \n",
"0 0.330165 -6.829571 0.965922 -7.570825 2.918987 blues \n",
"1 -1.107202 -4.556555 -2.436490 3.316913 -0.608485 blues \n",
"2 -6.660252 -14.682694 -11.719264 -11.025216 -13.387260 blues \n",
"3 -9.527455 -9.244394 -2.848274 -1.418707 -5.932607 blues \n",
"4 2.671134 -4.760879 -0.949005 0.024832 -2.005315 blues \n",
"\n",
"[5 rows x 28 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"metadata": {
"id": "iHrDHCaR9gKR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "7d32943a-1ad5-4a59-c13a-beebeb36e4c2"
},
"cell_type": "code",
"source": [
"data.shape"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1000, 28)"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"metadata": {
"id": "veD5BgX49hZa",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Dropping unneccesary columns\n",
"data = data.drop(['filename'],axis=1)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "Nyr0aAAsGXjZ",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Encoding the Labels"
]
},
{
"metadata": {
"id": "frI5HH4q-1HS",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"genre_list = data.iloc[:, -1]\n",
"encoder = LabelEncoder()\n",
"y = encoder.fit_transform(genre_list)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "Slm8W0-iGVhI",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
""
]
},
{
"metadata": {
"id": "_2n8a02zGfvP",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Scaling the Feature columns"
]
},
{
"metadata": {
"id": "uqcqn-nyAofk",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"scaler = StandardScaler()\n",
"X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype = float))"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "e3VZvbwpGo9R",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Dividing data into training and Testing set"
]
},
{
"metadata": {
"id": "F1GW3VvQA7Rj",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "upuczQ-KBHJ5",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "1431a28b-e8b6-4db2-e505-7e149e37c0d7"
},
"cell_type": "code",
"source": [
"len(y_train)"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"800"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"metadata": {
"id": "LtoE_FqqBzM8",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "76555a2b-2030-48e1-b52d-d71b4ebae38e"
},
"cell_type": "code",
"source": [
"len(y_test)"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"200"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"metadata": {
"id": "ir9XaWgQB0lq",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "2ec90814-19d8-4f27-934a-1ce54406d4ea"
},
"cell_type": "code",
"source": [
"X_train[10]"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([-0.9149113 , 0.18294103, -1.10587131, -1.3875197 , -1.14640873,\n",
" -0.97232926, -0.29174214, 1.20078936, -0.68458101, -0.55849017,\n",
" -1.27056582, -0.88176926, -0.74844069, -0.40970382, 0.49685952,\n",
" -1.12666045, 0.59501437, -0.39783853, 0.29327275, -0.72916871,\n",
" 0.63015786, -0.91149976, 0.7743942 , -0.64790051, 0.42229852,\n",
" -1.01449461])"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"metadata": {
"id": "Vp2yc5FWG04e",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Classification with Keras\n",
"\n",
"## Building our Network"
]
},
{
"metadata": {
"id": "Qj3sc2uFEUMt",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from keras import models\n",
"from keras import layers\n",
"\n",
"model = models.Sequential()\n",
"model.add(layers.Dense(256, activation='relu', input_shape=(X_train.shape[1],)))\n",
"\n",
"model.add(layers.Dense(128, activation='relu'))\n",
"\n",
"model.add(layers.Dense(64, activation='relu'))\n",
"\n",
"model.add(layers.Dense(10, activation='softmax'))"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "7yrsmpI6EjJ2",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"model.compile(optimizer='adam',\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "bP0hVm4aElS7",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 697
},
"outputId": "aacf234d-d0a9-4de4-91be-5fd45a33b279"
},
"cell_type": "code",
"source": [
"history = model.fit(X_train,\n",
" y_train,\n",
" epochs=20,\n",
" batch_size=128)\n",
" "
],
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"800/800 [==============================] - 1s 811us/step - loss: 2.1289 - acc: 0.2400\n",
"Epoch 2/20\n",
"800/800 [==============================] - 0s 39us/step - loss: 1.7940 - acc: 0.4088\n",
"Epoch 3/20\n",
"800/800 [==============================] - 0s 37us/step - loss: 1.5437 - acc: 0.4450\n",
"Epoch 4/20\n",
"800/800 [==============================] - 0s 38us/step - loss: 1.3584 - acc: 0.5413\n",
"Epoch 5/20\n",
"800/800 [==============================] - 0s 38us/step - loss: 1.2220 - acc: 0.5750\n",
"Epoch 6/20\n",
"800/800 [==============================] - 0s 41us/step - loss: 1.1187 - acc: 0.6288\n",
"Epoch 7/20\n",
"800/800 [==============================] - 0s 37us/step - loss: 1.0326 - acc: 0.6550\n",
"Epoch 8/20\n",
"800/800 [==============================] - 0s 44us/step - loss: 0.9631 - acc: 0.6713\n",
"Epoch 9/20\n",
"800/800 [==============================] - 0s 47us/step - loss: 0.9143 - acc: 0.6913\n",
"Epoch 10/20\n",
"800/800 [==============================] - 0s 37us/step - loss: 0.8630 - acc: 0.7125\n",
"Epoch 11/20\n",
"800/800 [==============================] - 0s 36us/step - loss: 0.8095 - acc: 0.7263\n",
"Epoch 12/20\n",
"800/800 [==============================] - 0s 37us/step - loss: 0.7728 - acc: 0.7700\n",
"Epoch 13/20\n",
"800/800 [==============================] - 0s 36us/step - loss: 0.7433 - acc: 0.7563\n",
"Epoch 14/20\n",
"800/800 [==============================] - 0s 45us/step - loss: 0.7066 - acc: 0.7825\n",
"Epoch 15/20\n",
"800/800 [==============================] - 0s 43us/step - loss: 0.6718 - acc: 0.7787\n",
"Epoch 16/20\n",
"800/800 [==============================] - 0s 36us/step - loss: 0.6601 - acc: 0.7913\n",
"Epoch 17/20\n",
"800/800 [==============================] - 0s 36us/step - loss: 0.6242 - acc: 0.7963\n",
"Epoch 18/20\n",
"800/800 [==============================] - 0s 44us/step - loss: 0.5994 - acc: 0.8038\n",
"Epoch 19/20\n",
"800/800 [==============================] - 0s 42us/step - loss: 0.5715 - acc: 0.8125\n",
"Epoch 20/20\n",
"800/800 [==============================] - 0s 39us/step - loss: 0.5437 - acc: 0.8250\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "0m1J0_wUFK4C",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "ffd3bf36-29ea-437a-987c-9aa600b9dae6"
},
"cell_type": "code",
"source": [
"test_loss, test_acc = model.evaluate(X_test,y_test)"
],
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": [
"200/200 [==============================] - 0s 244us/step\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "f6HrjXeUF0Ko",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "ea282dbd-6f9e-48c7-de2d-dc9afde8949e"
},
"cell_type": "code",
"source": [
"print('test_acc: ',test_acc)"
],
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"text": [
"test_acc: 0.68\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "3yQmP_f5Kq0w",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Tes accuracy is less than training dataa accuracy. This hints at Overfitting"
]
},
{
"metadata": {
"id": "-U2qzRJoHV9O",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Validating our approach\n",
"Let's set apart 200 samples in our training data to use as a validation set:"
]
},
{
"metadata": {
"id": "xJNbvYZoF7ZT",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"x_val = X_train[:200]\n",
"partial_x_train = X_train[200:]\n",
"\n",
"y_val = y_train[:200]\n",
"partial_y_train = y_train[200:]"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "L1EkG59EHeEV",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Now let's train our network for 20 epochs:"
]
},
{
"metadata": {
"id": "Dp3G4P3aP4k2",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1071
},
"outputId": "25e1a389-1ac2-425b-bd5f-05736b6e9b96"
},
"cell_type": "code",
"source": [
"model = models.Sequential()\n",
"model.add(layers.Dense(512, activation='relu', input_shape=(X_train.shape[1],)))\n",
"model.add(layers.Dense(256, activation='relu'))\n",
"model.add(layers.Dense(128, activation='relu'))\n",
"model.add(layers.Dense(64, activation='relu'))\n",
"model.add(layers.Dense(10, activation='softmax'))\n",
"\n",
"model.compile(optimizer='adam',\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(partial_x_train,\n",
" partial_y_train,\n",
" epochs=30,\n",
" batch_size=512,\n",
" validation_data=(x_val, y_val))\n",
"results = model.evaluate(X_test, y_test)"
],
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 600 samples, validate on 200 samples\n",
"Epoch 1/30\n",
"600/600 [==============================] - 1s 1ms/step - loss: 2.3074 - acc: 0.0950 - val_loss: 2.1857 - val_acc: 0.2850\n",
"Epoch 2/30\n",
"600/600 [==============================] - 0s 65us/step - loss: 2.1126 - acc: 0.3783 - val_loss: 2.0936 - val_acc: 0.2400\n",
"Epoch 3/30\n",
"600/600 [==============================] - 0s 59us/step - loss: 1.9535 - acc: 0.3633 - val_loss: 1.9966 - val_acc: 0.2600\n",
"Epoch 4/30\n",
"600/600 [==============================] - 0s 58us/step - loss: 1.8082 - acc: 0.3833 - val_loss: 1.8713 - val_acc: 0.3250\n",
"Epoch 5/30\n",
"600/600 [==============================] - 0s 59us/step - loss: 1.6663 - acc: 0.4083 - val_loss: 1.7302 - val_acc: 0.3450\n",
"Epoch 6/30\n",
"600/600 [==============================] - 0s 52us/step - loss: 1.5329 - acc: 0.4550 - val_loss: 1.6233 - val_acc: 0.3700\n",
"Epoch 7/30\n",
"600/600 [==============================] - 0s 62us/step - loss: 1.4236 - acc: 0.4850 - val_loss: 1.5402 - val_acc: 0.3950\n",
"Epoch 8/30\n",
"600/600 [==============================] - 0s 57us/step - loss: 1.3250 - acc: 0.5117 - val_loss: 1.4655 - val_acc: 0.3800\n",
"Epoch 9/30\n",
"600/600 [==============================] - 0s 52us/step - loss: 1.2338 - acc: 0.5633 - val_loss: 1.3927 - val_acc: 0.4650\n",
"Epoch 10/30\n",
"600/600 [==============================] - 0s 61us/step - loss: 1.1577 - acc: 0.5983 - val_loss: 1.3338 - val_acc: 0.5500\n",
"Epoch 11/30\n",
"600/600 [==============================] - 0s 64us/step - loss: 1.0981 - acc: 0.6317 - val_loss: 1.3111 - val_acc: 0.5550\n",
"Epoch 12/30\n",
"600/600 [==============================] - 0s 52us/step - loss: 1.0529 - acc: 0.6517 - val_loss: 1.2696 - val_acc: 0.5400\n",
"Epoch 13/30\n",
"600/600 [==============================] - 0s 52us/step - loss: 0.9994 - acc: 0.6567 - val_loss: 1.2480 - val_acc: 0.5400\n",
"Epoch 14/30\n",
"600/600 [==============================] - 0s 65us/step - loss: 0.9673 - acc: 0.6633 - val_loss: 1.2384 - val_acc: 0.5700\n",
"Epoch 15/30\n",
"600/600 [==============================] - 0s 58us/step - loss: 0.9286 - acc: 0.6633 - val_loss: 1.1953 - val_acc: 0.5800\n",
"Epoch 16/30\n",
"600/600 [==============================] - 0s 59us/step - loss: 0.8849 - acc: 0.6783 - val_loss: 1.2000 - val_acc: 0.5550\n",
"Epoch 17/30\n",
"600/600 [==============================] - 0s 61us/step - loss: 0.8621 - acc: 0.6850 - val_loss: 1.1743 - val_acc: 0.5850\n",
"Epoch 18/30\n",
"600/600 [==============================] - 0s 61us/step - loss: 0.8195 - acc: 0.7150 - val_loss: 1.1609 - val_acc: 0.5750\n",
"Epoch 19/30\n",
"600/600 [==============================] - 0s 62us/step - loss: 0.7976 - acc: 0.7283 - val_loss: 1.1238 - val_acc: 0.6150\n",
"Epoch 20/30\n",
"600/600 [==============================] - 0s 63us/step - loss: 0.7660 - acc: 0.7650 - val_loss: 1.1604 - val_acc: 0.5850\n",
"Epoch 21/30\n",
"600/600 [==============================] - 0s 65us/step - loss: 0.7465 - acc: 0.7650 - val_loss: 1.1888 - val_acc: 0.5700\n",
"Epoch 22/30\n",
"600/600 [==============================] - 0s 65us/step - loss: 0.7099 - acc: 0.7517 - val_loss: 1.1563 - val_acc: 0.6050\n",
"Epoch 23/30\n",
"600/600 [==============================] - 0s 68us/step - loss: 0.6857 - acc: 0.7683 - val_loss: 1.0900 - val_acc: 0.6200\n",
"Epoch 24/30\n",
"600/600 [==============================] - 0s 67us/step - loss: 0.6597 - acc: 0.7850 - val_loss: 1.0872 - val_acc: 0.6300\n",
"Epoch 25/30\n",
"600/600 [==============================] - 0s 67us/step - loss: 0.6377 - acc: 0.7967 - val_loss: 1.1148 - val_acc: 0.6200\n",
"Epoch 26/30\n",
"600/600 [==============================] - 0s 64us/step - loss: 0.6070 - acc: 0.8200 - val_loss: 1.1397 - val_acc: 0.6150\n",
"Epoch 27/30\n",
"600/600 [==============================] - 0s 66us/step - loss: 0.5991 - acc: 0.8167 - val_loss: 1.1255 - val_acc: 0.6300\n",
"Epoch 28/30\n",
"600/600 [==============================] - 0s 62us/step - loss: 0.5656 - acc: 0.8333 - val_loss: 1.0955 - val_acc: 0.6350\n",
"Epoch 29/30\n",
"600/600 [==============================] - 0s 66us/step - loss: 0.5513 - acc: 0.8300 - val_loss: 1.1030 - val_acc: 0.6050\n",
"Epoch 30/30\n",
"600/600 [==============================] - 0s 56us/step - loss: 0.5498 - acc: 0.8233 - val_loss: 1.0869 - val_acc: 0.6250\n",
"200/200 [==============================] - 0s 65us/step\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "dljqHfDPI6lH",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
""
]
},
{
"metadata": {
"id": "Mvi9it1SI4aR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "98b01ef2-3935-442b-82d6-45f56e036d39"
},
"cell_type": "code",
"source": [
"results"
],
"execution_count": 38,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[1.2261371064186095, 0.65]"
]
},
"metadata": {
"tags": []
},
"execution_count": 38
}
]
},
{
"metadata": {
"id": "r3hb8s1l4rBA",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Predictions on Test Data"
]
},
{
"metadata": {
"id": "gudBAhIXJIi2",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"predictions = model.predict(X_test)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "Xb7bVPSwJQF0",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "aca09c75-1d21-4847-bdd9-a0521dc8d948"
},
"cell_type": "code",
"source": [
"predictions[0].shape"
],
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(10,)"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"metadata": {
"id": "llusRQV0JRy9",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "a856289d-883a-47cb-c0fb-ec148330a60a"
},
"cell_type": "code",
"source": [
"np.sum(predictions[0])"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"metadata": {
"id": "0eoEuSZqJTdU",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "94c17d00-dd7f-40a1-84d2-78d1ebde6103"
},
"cell_type": "code",
"source": [
"np.argmax(predictions[0])"
],
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"8"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"metadata": {
"id": "Utgt1bXfJVRN",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
@akkisapra
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Hi
Do you have dataset available with you ?? seems author removed GTZAN datasets..
Thanks in advance

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ghost commented Jan 8, 2019

Hi
Do you have dataset available with you ?? seems author removed GTZAN datasets..
Thanks in advance

GTZAN dataset:
http://opihi.cs.uvic.ca/sound/genres.tar.gz

@Pravirk22
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can you send us loaction of your path. i am not getting the logic

@gjnehruceg33
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gjnehruceg33 commented Mar 9, 2019 •
edited
Loading

header = 'filename chroma_stft spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'
for i in range(1, 21):
header += f' mfcc{i} '
header += ' label '
header = header.split()

i have got syntax errror

how to fix the pblm??help me

File "", line 3
header += f' mfcc{i} '
^
SyntaxError: invalid syntax

@atulgiri
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In case any one having error with the rmse just include rmse = librosa.feature.rmse(y=y) in the 4th block code.

@rhymiz
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rhymiz commented May 25, 2019 •
edited
Loading

@gjnehruceg33 are you using Python 3.6+? If not, you'll be seeing that syntax error.
You should be able to change header += f' mfcc{i} ' to header += ' mfcc{0} '.format(i) and that should work on Python 2.

@ShangQingLiu
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In case any one having error with the rmse just include rmse = librosa.feature.rmse(y=y) in the 4th block code.

Notice that after 0.6.3 version change librosa.feature.rmse to librose.feature.rms.

@sileshiman
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Nice work

@atharvaathaley
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How to make gui for this?

@MingShyanWei
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In case any one having error with the rmse just include rmse = librosa.feature.rmse(y=y) in the 4th block code.

Notice that after 0.6.3 version change librosa.feature.rmse to librose.feature.rms.

librosa.feature.rms <=

@itrare
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itrare commented Nov 2, 2019

Where did you use those images, I just wanted to know are they just for visualization?

@basriciftci
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You extracted all spectrograms and saved them as figs, but you don't need them for training? Then, what was the point there?

@parulnith
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Author

You extracted all spectrograms and saved them as figs, but you don't need them for training? Then, what was the point there?

Hi @basriciftci, good question. Like I have mentioned in the article accompanying the code(https://towardsdatascience.com/music-genre-classification-with-python-c714d032f0d8), the purpose of this whole exercise was to help beginners understand the concept of working and understanding the audio files. Once the data has been extracted, they can use any algorithm of their choice to train it.

@Markjohnyaa
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hello,
when i test on genres it work fine , when i test on my set of songs it did not work . I am using two type of songs , it read one type but did not read other part for example when print (y) : 000000000000000000000000000000000000 ... is the printed but 111111111111111111111 is missing . would you please solve this issue . thanks

@DylanCarey94
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Hi @parulnith. I am wondering have you any example where you used the Spectogram images for training the Neural network and classifying?

@michellemul
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Hi @parulnith. I am wondering have you any example where you used the Spectogram images for training the Neural network and classifying?

Hi @DylanCarey94, did you manage to create a neural network using the spectogram images?

@DylanCarey94
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DylanCarey94 commented Mar 10, 2020 via email

@specpro30
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Hi there, I ran through the code and for the code block: np.argmax(predictions[0])

My result was 3. I noticed in the example that it is 8. Does this mean something is wrong with my model?

How can we start using this to pass it new songs to predict?

@ndujar
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ndujar commented Apr 19, 2020

Hi @specpro30,

Just put replace the data in X_test with whatever features you extract from your new songs and you are good to go :)
Don't forget to scale before using the scaler
X_new = scaler.fit_transform(np.array(new_data, dtype = float))
predictions = model.predict(X_new)

@SonamSangpoLama
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SonamSangpoLama commented May 2, 2020 •
edited
Loading

For this code, I am getting problem of "keras scratch graph"

history = model.fit(X_train, y_train, epochs=10, batch_size=32)

InvalidArgumentError: Received a label value of 996 which is outside the valid range of [0, 10). Label values: 807 996 153 945 283 178 816 976 923 648 129 22 944 439 34 979 288 994 321 483 810 830 215 736 324 138 308 796 473 824 206 627
[[node loss_7/dense_24_loss/sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_6597]

Function call stack:
keras_scratch_graph

CAN SOMEONE HELP ME TO SORT OUT THIS PROBLEM

@ndujar
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ndujar commented May 2, 2020

Hi @SonamSangpoLama, I think you want to check the contents of your y_train vector. There is too many classes in it. It can only be a number between 0 and 10:

The dataset consists of 10 genres i.e

Blues
Classical
Country
Disco
Hiphop
Jazz
Metal
Pop
Reggae
Rock

@SonamSangpoLama
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Hi @SonamSangpoLama, I think you want to check the contents of your y_train vector. There is too many classes in it. It can only be a number between 0 and 10:

The dataset consists of 10 genres i.e

Blues
Classical
Country
Disco
Hiphop
Jazz
Metal
Pop
Reggae
Rock

Thank you for your kind response..

Can you explain me further. I cannot get it..How can I check y_train content..?

@ndujar
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ndujar commented May 3, 2020 •
edited
Loading

Hi @SonamSangpoLama,
Can you share what does the 'label' column look like in your dataset?
Maybe you took a dataset with too many classes. Can you share the output of this?:
data = pd.read_csv('data.csv')
display(data.head())
print(data['label'].unique())

@SonamSangpoLama
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SonamSangpoLama commented May 6, 2020 •
edited
Loading

I have just been trying to use the same code of above but I am getting error. I have just made tiny changes on file directory.

Extracting spec from audios
cmap = plt.get_cmap('inferno')
plt.figure(figsize=(10,10))
genres = 'blues classical country disco hiphop jazz metal pop reggae rock'.split()
for g in genres:
pathlib.Path(f'image_genres/{g}').mkdir(parents=True, exist_ok=True)
for filename in os.listdir(f'./genres/{g}'):
songname = f'./genres/{g}/{filename}'
y, sr = librosa.load(songname, mono=True, duration=5)
plt.specgram(y, NFFT=2048, Fs=2, Fc=0, noverlap=128, cmap=cmap, sides='default', mode='default', scale='dB');
plt.axis('off');
plt.savefig(f'image_genres/{g}/{filename[:-3].replace(".", "")}.png')
plt.clf()

extracting features from spect

header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'
for i in range(1, 21):
header += f' mfcc{i}'
header += ' label'
header = header.split()

writing to csv

file = open('data.csv', 'w', newline='')
with file:
writer = csv.writer(file)
writer.writerow(header)
genres = 'blues classical country disco hiphop jazz metal pop reggae rock'.split()
for g in genres:
for filename in os.listdir(f'./genres/{g}'):
songname = f'./genres/{g}/{filename}'
y, sr = librosa.load(songname, mono=True, duration=30)
chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr)
spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
zcr = librosa.feature.zero_crossing_rate(y)
mfcc = librosa.feature.mfcc(y=y, sr=sr)
to_append = f'{filename} {np.mean(chroma_stft)} {np.mean(spec_cent)} {np.mean(spec_bw)} {np.mean(rolloff)} {np.mean(zcr)}'
for e in mfcc:
to_append += f' {np.mean(e)}'
to_append += f' {g}'
file = open('data.csv', 'a', newline='')
with file:
writer = csv.writer(file)
writer.writerow(to_append.split())

reading csv

data = pd.read_csv('data.csv')
data.head()

standard scaler

scaler = StandardScaler()
X = scaler.fit_transform(np.array(data.iloc[:, :1], dtype = float))

@Dhruv28112000
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What was the use of spectrogram images???

@jkotra
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jkotra commented Oct 31, 2020

I wrote a easy to understand notebook based on FE ideas in this one:
https://github.com/jkotra/MusicGenreClassification/blob/master/MusicGenreClassification_FeatureEnsemble.ipynb

Take a look if this seems too complicated 😉

@zebrapol
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zebrapol commented Jan 7, 2022

Hello @ndujar.
Could you please describe in more detail how to me predictions, namely the arrays that are displayed during prediction, turn into genres. I will be very grateful!

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