- Arushi Singhal (201516178)
- Avani Agrawal (201530194)
- Simran Singhal (201516190)
- Rachavarapu Kranthi Kumar
Automatic musical instrument recognition of monophonic and monotimbral music instruments sound signals. Instrument recognition is done using classification techniques.
In particular, the developed method taking into 7 instrumental categories including Cello (889 samples), Clarinet (846 samples), Flute (878 samples), Guitar (106 samples), Saxophone (732 samples),Trumpet (485 samples), Violin (1502 samples).
Dataset taken from :- http://www.philharmonia.co.uk/explore/sound_samples/banjo
Research Paper considered:- http://cs229.stanford.edu/proj2015/010_report.pdf
Dataset of the project :- https://drive.google.com/file/d/17MlMlGGeRQtYVuUCixoHjkLYU_3RuNu5/view
Link of Presentation:- https://docs.google.com/presentation/d/1BtmrDHZg5RXMCW-lMgZ1cG7ZDpiGClWmtBwMR-UAtv8/edit?usp=sharing
Guitar dataset :- https://drive.google.com/file/d/1Un5UpwG4Md8_Uh5uZ3B17LHLBn9yoi9j/view?usp=sharing
Dataset :- https://drive.google.com/file/d/1yQXwzNDFHXOEQgaXcTDoq3zl_m83JDAv/view?usp=sharing
Dataset with Noise:- https://drive.google.com/file/d/1e5Z1NMe0KQEeVzpwE7a-9DnekFxEyNVI/view?usp=sharing
python svm_audio.py features.csv