MUSIC RECOGNITION USING DEEP LEARNING

Abstract

In our daily lives, we often listen to songs that we like and enjoy. However, there may be instances when we are in transit or at venues such as clubs or restaurants, and we hear a song playing in the background that catches our attention. We may desire to listen to this song again at a later time, but unfortunately, we may not be aware of the title of the song. As a result, we are unable to locate and listen to it again. Our project, titled “Music Recognition Using Deep Learning,” aims to provide a convenient solution for identifying songs that are heard in various locations. While there are several existing popular applications, such as Shazam, SoundHound, and Google Sound Search, which offer music recognition services, we conducted an in-depth study of papers related to these apps to identify appropriate technologies and algorithms for our project. Our project employs a deep neural network that leverages a contrastive learning approach for the purpose of song recognition. Initially, a large collection of songs is gathered and subjected to signal processing techniques, including Short Time Fourier Transform (STFT), mel filter bank, and decibel scale to generate log mel-spectrograms. These log mel-spectrograms are then fed into the neural network, which is trained to generate a fingerprint for each song at the segment level. These fingerprints are stored in a database.