Audio files comparator using wavelet transform and similarity metrics - Lee, Da Long (2025)
This project is a development-based project revolving around signal processing. The aim of this project is to develop a program that utilizes continuous wavelet transform (CWT) for audio similarity recognition. Its primary objective is to identify the similarities among audio files with different information such as file names or formats. In today’s diverse musical landscape, songs undergo various interpretations, covered in different languages, or rendered using a myriad of instruments. Compositions may span the spectrum, ranging from performances with real musical instruments to those composed solely of synthesized sounds, typically electronic dance music (EDM). Furthermore, songs exhibit versatility in their presentation, ranging from vocal renditions accompanied by instruments to whistling, humming or acapella performances. The evolution of music has also fostered the emergence of mashups and remixes, where distinct tracks seamlessly blend together to create new compositions. Despite these variations, the tunes or pitches of songs remain recognizable to the human ear and even audio detection algorithms. With the proliferation of digital music, people download songs from music applications or the internet, whether for personal listening in vehicles or to play in parties. However, these downloaded songs may vary depending on their file names and formats. Consequently, this project aims to identify identical or akin songs with various information and display out the percentage of differences between the audio files. The project’s methodology centres on Python programming, where comparisons of audio similarities will be conducted.