AES Convention Papers Forum

A Robust Audio Feature Extraction Algorithm for Music Identification

Document Thumbnail

In this paper, we describe a novel audio feature extraction method, which can effectively improve the performance of music identification under noisy circumstances. It is based on a dual box approach that extracts from the sound spectrogram point clusters with significant energy variation. This approach was tested in a song finder application that can identify music from samples recorded by microphone in the presence of dominant noise. A series of experiments show that under noisy circumstances, our system outperforms current state-of-the-art music identification algorithms and provides very good precision, scalability and query efficiency.

AES Convention: Paper Number:
Publication Date:

Click to purchase paper as a non-member or you can login as an AES member to see more options.

No AES members have commented on this paper yet.

Subscribe to this discussion

RSS Feed To be notified of new comments on this paper you can subscribe to this RSS feed. Forum users should login to see additional options.

Start a discussion!

If you would like to start a discussion about this paper and are an AES member then you can login here:

If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.

AES - Audio Engineering Society