Music instrument recognition using classification techniques.

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Music Instrument Classification using Machine Learning Algorithm

Using spectral features for Instrument Classification in polyphonic musical clips

In this project we use a Lightweight-CNN based model to classify instruments from the Freesound audio data set. We make use of Mel-Spectrogram features from the input audio data as the input to the CNN model. To add robustness to the model, we use a novel data augmentation technique based on the Cut-Mix algorithm.

AI model that generates a Music Video from a .wav file, using Instrument Classification, Speech Transcription, and Video Generation.

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This project involves classifying musical instruments given a sample of music. The goal was to determine which instrument (e.g. trumpet, violin, piano) is playing.

Emotion and Instrument audio classification of Indian Classical Music recordings using wav2vec2 transformer networks

Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music, NTUA

Bali has a diversity of arts that has been recognized by the world, where one of the most famous Balinese arts is the Karawitan art, especially the Kendang Tunggal instrument. Notation documentation or more commonly known as music transcription, can make learning a song easier, and in the case of this research, it makes it easier to learn to play the Kendang Tunggal instrument. The first approach method used to document a kendang tunggal song is onset detection. Onset is when the signal experiences an attack period, which helps segment the sound color of the drum instrument. The segmented kendang tunggal sound color classification uses the Backpropagation algorithm with several features of the frequency domain and time domain as a characteristic of the sound color. Then the kendang tunggal song is revived into a synthetic sound with the Mel Spectral Approximation filter. Based on the research, the optimal parameter for drum sound color segmentation with onset detection is the hop size 110 with normalization of the features on its onset detection function. The optimal backpropagation architecture obtained with a learning rate of 0.9, neurons 10, and epoch 2000 produces an accuracy of 60.85%. The synthesis method using the Mel Log Spectrum Approximation can make synthetic sounds similar to kendang songs with an accuracy of 83.33%

Identification of predominant musical instrument in a 3s audio excerpt.

Musical Instrument Spectrograms Classification using Transfer Learning

The group project for Compressed Sensing and Sparse Recovery 25 Spring

Explored 3 different multi-class classifiers on music instrument classification: CNN, KNN and DNN.

A collection of scripts (python, shell scripts) created for the Master Thesis in Sound and Music Computing titled 'Automatic classification of Musical Instrument samples' presented at the Music Technology Group, department of Information and Communication technology of the university Pompeu Fabra, Barcelona.

Automatic musical instrument recognition is subjected to active research which addresses problems like automatic music annotation, genre classification, emotion classification and artist recognition. Segregation of features from sound samples plays a vital role in auditory recognition. This research project focuses on predicting the categories of musical instruments, by building three different classifiers (XGBoost, Random Forest, AdaBoost) using a new approach for selecting the optimum features from the music audio files. It was conducted using 1500 sample audio files from five different orchestra music instruments (Bassoon, Double bass, French Horn, Trombone, Viola). We have extracted the features from the audio samples namely MFCC, ZCR and selected the prominent features using Boruta feature selection algorithm. This research produces the accuracy of 87.92% with XGBoost using Boruta feature selection which outplays the other models.

Indian classical music is the music of the Indian subcontinent and is one of the oldest kinds of music. Although Indian music has progressed significantly, the fundamental components appear to remain the same as they were two thousand years ago. The raga is at the center of Indian classical music, and it is the language of the spirit. Novel Indian music, on the other hand, has been heavily impacted by Western music notation, which is based on equal mean tones, temperament scales, and the universal priority of harmony. Indian and Western music differs not just in terms of culture, but also in terms of fundamental structures, scales, and tuning. New musical innovations are burgeoning in the current decade with an incredible number of fusions of Western Classical music and Indian Classical music.While fusions are a way of celebrating two different art forms, is the originality of the classical forms fading away? This project aimsatstudyingthe classification of Indian Classical Music and Western Classical music.A simple feedforward neural network was created to classify the audio data.The audio classification is not limited to the style of music. The model also classifies the audios according to the instruments and the different ensembles at play.A testing accuracy of 75.00 percent and a training accuracy of 97.9percent was achieved through the model.This project is an attempt to encourage musical fusions and to help create awareness among the community about the ancient and widely practiced art of Carnatic Music.

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Musical Instrument Classification Project Using Beaglebone Black

Convolutional NN with keras/tensorflow to recognize classical instruments using the spectograms of musical notes.

Classificator for musical instruments made with deep neural networks.

Speech Science, Language Technology project (with Haoyun Wang)

Simple Machine learning program of Musical Instrument Classification for IRMAS

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A musical instrument classifier that identifies 30 different instruments using a CNN-based MobileNetV2 architecture, demonstrating end-to-end deep learning implementation.

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Course Project of EE679

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This is a repo containing the code used for extraction of features of musical instruments and store it in an excel sheet