Deep Neural Network and SVM-Based Multiclass Musical Instruments Classification Using MFCC, STFT, and Related Acoustic Features

Abstract

Artificial Intelligence (AI) has revolutionized the field of music analysis by enabling advanced sound recognition and classification techniques. In the recent year, the music industry has had transformative evolution in recent years, significantly impacting user engagement, creativity, and technological innovation across various domains, including entertainment, education, and therapy. Musical instrument recognition is an emerging field within this landscape that could be used in applications such as automated music transcription and intelligent recommendation systems and adaptive music generation. Using Deep Learning (DL) alongside Artificial Intelligence has dramatically improved how we hear things as it has become a robust analytics tool for patterns in audio. The intricate signals from audio data together with overlapping frequencies and instrument diversity present significant challenges to accurate musical instrument prediction and classification. Traditional structured machine learning models cannot be applied successfully to dealing with complicated patterns, in contrast DL models possess superior designed system architectures and stronger feature extracting ability. Through the integration of these DNN with default layers with support vector machines, an instrument recognition framework is presented in this research, on publicly available dataset of diverse instruments with three second clips combined with Mel Spectrogram and its audio features. The standard measures are used for measuring performance of models such as accuracy, precision, recall and f1-score. The proposed model DNN achieves 98% classification precision over the SVM baseline with accuracy of 96% using musical instrument dataset with 24 classes. Using this research as the base of concept, it is shown that DL is better than current proposed methodologies at improving audio transformation processes, and it promises potential in improving the state of the art at musical instrument identification techniques that would yield useful results to intelligent music systems and AI audio analysis methodologies.