Using Machine Learning to Study Music.
Classifying musical instruments with supervised machine learning, using K-nearest neighbors algorithm.
- Install dependencies as
pip3 install -r requirements.txt
gavy42@jarvis:~/MozartFlow/instrument_classification$ python3 model.py -h
usage: model.py [-h] [-k KNN] [-ll {DEBUG,INFO,WARNING,ERROR,CRITICAL}]
[-p PATH]
MozartFlow: Observing the flow of music.
optional arguments:
-h, --help show this help message and exit
-k KNN, --knn KNN K in K-nearest neighbours algorithm
-ll {DEBUG,INFO,WARNING,ERROR,CRITICAL}, --loglevel {DEBUG,INFO,WARNING,ERROR,CRITICAL}
Set the logging level
-p PATH, --path PATH Filepath of the audio file, need to be labeled
gavy42@jarvis:~/MozartFlow/instrument_classification$ - Dataset used to train this classifier was collected from London Philharmonic Orchestra Dataset.
- Each audio file records one note from one of the five instruments and has a length from 0.25 seconds to 6 seconds. In total, over 600 audio pieces were used to train the classifier, and the distribution of each class was roughly the same.
For copyright reasons, the original data is not included in this repository. But it is free to download.
- Instruments available are
["banjo", "bass clarinet", "bassoon", "cello", "clarinet", "contrabassoon", "cor anglais", "double bass", "flute", "french horn":, "guitar", "mandolin", "oboe", "percussion", "saxophone", "trombone", "trumpet", "tuba", "viola", "violin"].
The music pieces have their leading and ending silence trimmed. The threshold of trimming is 0.001 - if the intensity of the sound in the frame is below 0.1% of the highest sound intensity in the audio file, then the frame is trimmed out.
The Mel Frequency Cepstral Coefficents (MFCCs) of each music piece was extracted using Librosa. For each audio file, its MFCCs are averaged to produce the final, length-20 feature vector.
K-nearest neighbors algorithm is used to train the classifier. And then predict the output of any given audio file.
[.]: Info[#]: Debug[*]: Status[!]: Warning[/!/]: Critical / Error
- This is still in implementation phase, read more about it here.
Recognizing gender using serveral machine learning algorithms.
[*] Accuracy on training set: 99.579 %
[*] Accuracy on test set: 98.106 %- Run
pip3 install -r requirements.txtto install the dependencies.
gavy42@jarvis:~/MozartFlow/gender_recognition$ python3 model.py -h
usage: model.py [-h] [-t TREES] [-p PREDICT] [-P]
[-L {DEBUG,INFO,WARNING,ERROR,CRITICAL}]
Gender Voice Recognition
optional arguments:
-h, --help show this help message and exit
-t TREES, --trees TREES
No of decision trees (default=5)
-p PREDICT, --predict PREDICT
Give a filepath and get prediction
-P, --predictnow Start speaking and get prediction
-L {DEBUG,INFO,WARNING,ERROR,CRITICAL}, --log {DEBUG,INFO,WARNING,ERROR,CRITICAL}
Set the logging level- Run
python3 model.pyto simply train and test the model (using random forest algorithm with default 5 decision trees), the results will be like
gavy42@jarvis:~/MozartFlow/gender_recognition$ python3 model.py
[*] Training over 3169 instances ...
[####################################] 100%
----------Accuracy-----------
[*] Accuracy on training set: 99.579 %
[*] Accuracy on test set: 98.106 %
gavy42@jarvis:~/MozartFlow/gender_recognition$- This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range).
Kory Becker for this labeled dataset which I found on kaggle.
Usage of the file with_librosa.py
gavy42@jarvis:~/MozartFlow/audio_separation$ python3 with_librosa.py -h
usage: with_librosa.py [-h] -P PATH [-p PLOT] [-s SAVE]
Audio Separation with Librosa
optional arguments:
-h, --help show this help message and exit
-P PATH, --path PATH Path to the audio file (.mp3/.wav)
-p PLOT, --plot PLOT Plot the spectrograms
-s SAVE, --save SAVE Save as individual files
gavy42@jarvis:~/MozartFlow/audio_separation$If you have any problem trying to understand the code, feel free to raise and issue or drop a mail here.
Any better approach in efficiency, or accuracy, or majorly to complete the implementation remained is appreciated. Feel free to raise an issue and then make a pull request in continuation.