Audio Fingerprinting System to Detect and Match Audio Recordings

Abstract

The emergence of a sizable volume of audio data has increased the requirement for audio retrieval, which can identify the required information rapidly and reliably. Audio fingerprint retrieval is a preferable substitute due to its improved performance. The task of song identification from an audio recording has been an ongoing research problem in the field of music information retrieval. This work presents a robust and efficient audio fingerprinting method for song detection. This approach for the proposed system utilizes a combination of spectral and temporal features extracted from the audio signal to generate a compact and unique fingerprint for each song. A matching algorithm is then used to compare the fingerprint of the query recording to those in a reference database and identify the closest match. The system is evaluated on a diverse dataset of commercial songs and a standardized dataset. The results demonstrate the superior identification accuracy of the proposed method compared to existing approaches on a standardized dataset. Additionally, the method shows comparable identification performance for recordings, particularly for shorter segments of 1 s, with an improvement in accuracy by 14%. Moreover, the proposed method achieves a reduction in storage space by 10% in terms of the number of fingerprints required.