Deep Neural Network for Musical Instrument Recognition using MFCCs

Autor:	Mahanta, Saranga Kingkor, Khilji, Abdullah Faiz Ur Rahman, Pakray, Partha
Rok vydání:	2021
Předmět:	Computer Science - Sound Computer Science - Artificial Intelligence Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Zdroj:	Computacion y Sistemas, Vol 25, No 2 (2021): 25(2) 2021
Druh dokumentu:	Working Paper
Popis:	The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the musical domain. Musical instrument recognition is the task of instrument identification by virtue of its audio. This audio, also termed as the sound vibrations are leveraged by the model to match with the instrument classes. In this paper, we use an artificial neural network (ANN) model that was trained to perform classification on twenty different classes of musical instruments. Here we use use only the mel-frequency cepstral coefficients (MFCCs) of the audio data. Our proposed model trains on the full London philharmonic orchestra dataset which contains twenty classes of instruments belonging to the four families viz. woodwinds, brass, percussion, and strings. Based on experimental results our model achieves state-of-the-art accuracy on the same. Comment: Was suggested to upload on a later date
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.00933 Zobrazit plný text záznamu View this record from Arxiv