Audio Similarity. Mark Zadel MUMT 611 March 8, Audio Similarity p.1/23

Transcription

1 Audio Similarity Mark Zadel MUMT 611 March 8, 2004 Audio Similarity p.1/23

2 Overview MFCCs Foote Content-Based Retrieval of Music and Audio (1997) Logan, Salomon A Music Similarity Function Based On Signal Analysis (2001) Audio Similarity p.2/23

3 Motivation MP3s, music on the Internet Large collections of songs How to search? Digital music libraries Commercial applications Audio Similarity p.3/23

4 MFCCs Mel-frequency cepstral coefficients Popular in speech analysis community Feature vector characterizing one frame of audio Gives the spectral envelope for the frame Emphasizes perceptual aspects: mel frequency scale, logarithmic amplitude Audio Similarity p.4/23

5 Computing MFCCs Audio Similarity p.5/23

6 Cepstrum From aronowc/speech/features.pdf Audio Similarity p.6/23

7 Mel Frequency Audio Similarity p.7/23

8 Mel Spectra Audio Similarity p.8/23

9 Foote Content-Based Retrieval of Music and Audio Assess acoustic similarity of audio segments, use this to search database System trained by human input Uses vector quantization Extract feature vectors, quantize, generate template Compute distance between templates Audio Similarity p.9/23

10 Foote: Procedure Audio Similarity p.10/23

11 Foote: Training Give a set of labeled examples to the system These labels drive tree-based quantization Training deemphasizes irrelevant information Audio Similarity p.11/23

12 Foote: Tree-Based Quantizer Feature space partitioned into cells Cells have maximally different class populations Recursively split space along each dimension Maximize mutual information probability that the different cells contain different classes Audio Similarity p.12/23

13 Foote: Tree-Based Quantizer Audio Similarity p.13/23

14 Foote: Comparing Templates Make a template (histogram) based on the frequency of each cell Similar templates will be close to each other Define distance: Euclidean distance, cosine distance Search: compute distance to audio samples in database, sort Audio Similarity p.14/23

15 Foote: Comparing Templates Audio Similarity p.15/23

16 Foote: Performance Audio Similarity p.16/23

17 Logan Music Similarity Automatically determine music similarity Builds on work of Foote. Differences: Histogram bins local to each song Uses Earth Mover s Distance Audio Similarity p.17/23

18 Logan: Procedure Compute signature based on spectral features Generate MFCCs Cluster using K-means technique Set of clusters (mean, covariance, weight) is song s signature NB: clustering is local to each song Compare signatures using EMD Audio Similarity p.18/23

19 Logan: K-means Clustering Randomly assign MFCCs to K clusers For each point Calculate distance to the centroid of each cluster Move it to the closest cluster Sum of distances smaller at each step Stop when no other moves required Clusters non-hierarchical, non-overlapping Every member closest to its own cluster Audio Similarity p.19/23

20 Logan: Earth Mover s Distance Calculates the minimum amout of work required to transform one signature into the other Cluster p i expressed as (µ pi, Σ pi, w pi ) Uses distance d pi q j (Kullback Leibler), flow f pi q j between clusters Solve for flow subject to constraints Minimize W = m i=1 n j=1 d p i q j f pi q j m n i=1 j=1 EMD(P, Q) = d p i q j f pi q j m n i=1 j=1 f p i q j Audio Similarity p.20/23

21 Logan: Performance Audio Similarity p.21/23

22 Logan: Performance Audio Similarity p.22/23

23 Further Reading Logan Mel Frequency Cepstral Coefficients for Music Modeling (2000) Logan Toward Evaluation Techniques for Music Similarity (2003) Liu, Huang Content-Based Indexing and Retrieval-By-Example in Audio (2000) Audio Similarity p.23/23