MUSIC RECOGNITION USING DEEP LEARNING
Date
2022-04
Authors
KHANAL, PRADEEP KUMAR
KAYASTHA, ACHYUT
KHATAKHO, ASHISH
Journal Title
Journal ISSN
Volume Title
Publisher
I.O.E. Pulchowk Campus
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.
Description
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.
Keywords
music recognition,, deep learning in music,, contrastive learning