Skip to content

M
musicinformationretrieval-com
Commit ef132c53 authored by Leigh Smith's avatar Leigh Smith
Browse files
Options

Upgraded cross-validation and created ToC link to evaluation

parent 14b2f280
Show whitespace changes
Inline Side-by-side
Showing with 305 additions and 9 deletions
Table_of_Contents.ipynb
View file @ ef132c53
{
"metadata": {
"name": "",
"signature": "sha256:02701b4647f4627969e1153bf3a0a52f55db3adcfa149e54f81fc4d27d65967b"
"signature": "sha256:536c517d48c98583d6306b6a68d4f93eed7dc8277f50f50aa7f338f8a2832ad9"
},
"nbformat": 3,
"nbformat_minor": 0,
......@@ -107,14 +107,23 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"1. [Cross Validation](notebooks/cross_validation.ipynb)\n",
"1. [Evaluation](notebooks/evaluation.ipynb)\n",
"1. [Tonal Descriptors: Pitch and Chroma](notebooks/tonal.ipynb)\n",
"1. [Feature Extraction](notebooks/feature_extraction.ipynb)\n",
"1. [Beat Tracking](notebooks/beat_tracking.ipynb)\n",
"1. [Tempo Estimation](notebooks/tempo_estimation.ipynb)\n",
"1. [Nonnegative Matrix Factorization](notebooks/nmf.ipynb)\n",
"1. [Cross Validation](notebooks/cross_validation.ipynb)\n",
"1. [Exercise: Source Separation using NMF](exercises/nmf_source_separation.ipynb)"
"1. [Exercise: Source Separation using NMF](exercises/nmf_source_separation.ipynb)\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
......
notebooks/cross_validation.ipynb
View file @ ef132c53
{
"metadata": {
"name": "",
"signature": "sha256:14bd75857e4b8f9809ba4d5f05dc624688b7b01756fb2f49422023301d45173f"
"signature": "sha256:b3f4da5b401fc3911a6fa14e446f660b141ede1e9c80ecd1b470edfb58fc648f"
},
"nbformat": 3,
"nbformat_minor": 0,
......@@ -41,6 +41,50 @@
"from sklearn import cross_validation\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn import preprocessing\n",
"from essentia.standard import MonoLoader\n",
"from essentia.standard import ZeroCrossingRate, CentralMoments, Spectrum, Windowing, Centroid, DistributionShape\n",
"import urllib2\n",
"import urllib\n",
"\n",
"def process_corpus(corpus_URL):\n",
" \"\"\"Read a list of files to process from the text file at corpusURL. Return a list of URLs\"\"\" \n",
" # Open and read each line\n",
" url_list_text_data = urllib2.urlopen(corpus_URL) # it's a file like object and works just like a file\n",
" for file_URL in url_list_text_data: # files are iterable\n",
" yield file_URL.rstrip()\n",
" \n",
"def spectral_features(filelist):\n",
" \"\"\"\n",
" Given a list of files, retrieve them, analyse the first 100mS of each file and return\n",
" a feature table.\n",
" \"\"\"\n",
" number_of_files = len(filelist)\n",
" number_of_features = 5\n",
" features = np.zeros([number_of_files, number_of_features])\n",
" sample_rate = 44100\n",
"\n",
" for file_index, url in enumerate(filelist):\n",
" print url\n",
" urllib.urlretrieve(url, filename='/tmp/localfile.wav')\n",
" audio = MonoLoader(filename = '/tmp/localfile.wav', sampleRate = sample_rate)()\n",
" zcr = ZeroCrossingRate()\n",
" hamming_window = Windowing(type = 'hamming') # we need to window the frame to avoid FFT artifacts.\n",
" spectrum = Spectrum()\n",
" central_moments = CentralMoments()\n",
" distributionshape = DistributionShape()\n",
" spectral_centroid = Centroid()\n",
"\n",
" frame_size = int(round(0.100 * sample_rate)) # 100ms\n",
" # Only do the first frame for now.\n",
" # TODO we should generate values for the entire file, probably by averaging the features.\n",
" current_frame = audio[0 : frame_size]\n",
" features[file_index, 0] = zcr(current_frame)\n",
" spectral_magnitude = spectrum(hamming_window(current_frame))\n",
" centroid = spectral_centroid(spectral_magnitude)\n",
" spectral_moments = distributionshape(central_moments(spectral_magnitude))\n",
" features[file_index, 1] = centroid\n",
" features[file_index, 2:5] = spectral_moments\n",
" return features\n",
"\n",
"def crossValidateKNN(features, labels):\n",
" \"\"\"\n",
......@@ -67,14 +111,14 @@
" \n",
" # SCALE\n",
" scaler = preprocessing.MinMaxScaler(feature_range = (-1, 1))\n",
" trainingFeatures = scaler.fit_transform(features.take(train_index, 0))\n",
" training_features = scaler.fit_transform(features.take(train_index, 0))\n",
" # BUILD NEW MODEL - ADD YOUR MODEL BUILDING CODE HERE...\n",
" model = KNeighborsClassifier(n_neighbors = 3)\n",
" model.fit(trainingFeatures, labels.take(train_index, 0))\n",
" model.fit(training_features, labels.take(train_index, 0))\n",
" # RESCALE TEST DATA TO TRAINING SCALE SPACE\n",
" testingFeatures = scaler.transform(features.take(test_index, 0))\n",
" testing_features = scaler.transform(features.take(test_index, 0))\n",
" # EVALUATE WITH TEST DATA - ADD YOUR MODEL EVALUATION CODE HERE\n",
" model_output = model.predict(testingFeatures)\n",
" model_output = model.predict(testing_features)\n",
" print(\"KNN prediction %s\" % model_output) # Debugging.\n",
" # CONVERT labels(test,:) LABELS TO SAME FORMAT TO COMPUTE ERROR \n",
" labels_test = labels.take(test_index, 0)\n",
......@@ -86,7 +130,250 @@
],
"language": "python",
"metadata": {},
"outputs": []
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"snares_URL = \"https://ccrma.stanford.edu/workshops/mir2014/SnareCorpus.txt\"\n",
"snare_file_list = [audio_file_URL for audio_file_URL in process_corpus(snares_URL)]\n",
"kicks_URL = \"https://ccrma.stanford.edu/workshops/mir2014/KickCorpus.txt\"\n",
"kick_file_list = [audio_file_URL for audio_file_URL in process_corpus(kicks_URL)]\n",
"all_files = snare_file_list + kick_file_list\n",
"\n",
"drum_labels = np.empty(len(all_files), np.int32)\n",
"num_of_snares = len(snare_file_list)\n",
"drum_labels[0:num_of_snares] = 1 # set labels to the first sample type, e.g. snare\n",
"drum_labels[num_of_snares: num_of_snares + len(kick_file_list)] = 2 # Set next set to the second sample type, e.g kick\n",
"print drum_labels\n",
"\n",
"drum_features = spectral_features(all_files)\n",
"print drum_features\n",
"\n",
"print crossValidateKNN(drum_features, drum_labels)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2]\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_01_01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_02_01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_04_01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_05_01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_06_01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_07_01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_08_01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_09_01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_10_01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/snare_mono.wav"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_01_V01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_02_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_03_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_04_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_05_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_06_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_07_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_08_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_09_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_10_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[[ 1.29251704e-01 2.20017299e-01 4.07947935e-02 8.77821267e-01\n",
" -8.77261162e-03]\n",
" [ 1.06349207e-01 2.09325567e-01 3.52719985e-02 7.70348966e-01\n",
" -3.22882414e-01]\n",
" [ 1.14965983e-01 1.98087305e-01 3.39526162e-02 8.53771806e-01\n",
" -1.91451073e-01]\n",
" [ 1.09977327e-01 1.89880371e-01 3.07796430e-02 9.61023569e-01\n",
" 2.17356920e-01]\n",
" [ 1.37641728e-01 2.61134952e-01 4.25882451e-02 6.05063379e-01\n",
" -4.61072683e-01]\n",
" [ 1.12925172e-01 2.12166250e-01 3.77560928e-02 9.84243870e-01\n",
" 2.55144835e-01]\n",
" [ 1.43990934e-01 2.32887402e-01 4.25517298e-02 8.88096571e-01\n",
" -2.55122185e-02]\n",
" [ 1.24036282e-01 2.34317511e-01 4.48079035e-02 8.41823637e-01\n",
" -9.77532864e-02]\n",
" [ 1.33560091e-01 2.40808740e-01 3.97951268e-02 7.26550639e-01\n",
" -2.08487749e-01]\n",
" [ 1.14058957e-01 2.19529420e-01 3.08201890e-02 9.37722743e-01\n",
" 6.40640974e-01]\n",
" [ 9.97732393e-03 4.34924886e-02 1.27002802e-02 3.57807994e+00\n",
" 1.42459507e+01]\n",
" [ 7.02947844e-03 2.78101377e-02 6.62755780e-03 4.85479450e+00\n",
" 2.90792427e+01]\n",
" [ 5.66893443e-03 1.62475649e-02 2.56283046e-03 7.87885380e+00\n",
" 8.96873932e+01]\n",
" [ 5.66893443e-03 3.63209695e-02 9.78391711e-03 3.97197366e+00\n",
" 1.83651676e+01]\n",
" [ 8.61677993e-03 2.82980110e-02 8.20268691e-03 5.25167370e+00\n",
" 3.25443840e+01]\n",
" [ 7.93650839e-03 2.40181480e-02 5.99115109e-03 5.35486269e+00\n",
" 3.59657173e+01]\n",
" [ 7.70975044e-03 2.50173863e-02 6.07906608e-03 5.63076735e+00\n",
" 3.99530830e+01]\n",
" [ 5.66893443e-03 3.24563682e-02 9.35256947e-03 4.67505932e+00\n",
" 2.59624958e+01]\n",
" [ 8.84353742e-03 3.49452570e-02 8.03499669e-03 4.01454067e+00\n",
" 2.00484810e+01]\n",
" [ 8.61677993e-03 2.26410441e-02 6.27955049e-03 5.77101851e+00\n",
" 3.90028992e+01]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Fold: 0\n",
"TRAIN: [ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n",
"TEST: [0 1]\n",
"KNN prediction [1 1]\n",
"Fold: 1\n",
"TRAIN: [ 0 1 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n",
"TEST: [2 3]\n",
"KNN prediction [1 1]\n",
"Fold: 2\n",
"TRAIN: [ 0 1 2 3 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n",
"TEST: [4 5]\n",
"KNN prediction [1 1]\n",
"Fold: 3\n",
"TRAIN: [ 0 1 2 3 4 5 8 9 10 11 12 13 14 15 16 17 18 19]\n",
"TEST: [6 7]\n",
"KNN prediction [1 1]\n",
"Fold: 4\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 10 11 12 13 14 15 16 17 18 19]\n",
"TEST: [8 9]\n",
"KNN prediction [1 1]\n",
"Fold: 5\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 8 9 12 13 14 15 16 17 18 19]\n",
"TEST: [10 11]\n",
"KNN prediction [2 2]\n",
"Fold: 6\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 14 15 16 17 18 19]\n",
"TEST: [12 13]\n",
"KNN prediction [2 2]\n",
"Fold: 7\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 16 17 18 19]\n",
"TEST: [14 15]\n",
"KNN prediction [2 2]\n",
"Fold: 8\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 18 19]\n",
"TEST: [16 17]\n",
"KNN prediction [2 2]\n",
"Fold: 9\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17]\n",
"TEST: [18 19]\n",
"KNN prediction [2 2]\n",
"cross validation error: 0.000000\n",
"cross validation accuracy: 1.000000\n",
"None\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment