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Bridging Image and Audio Retrieval Techniques: A Study on Classification and Processing

This paper explores the adaptation of image retrieval and classification techniques to audio processing. By applying proven image methods to the one-dimensional nature of audio, we aim to improve audio retrieval outcomes. We investigate various histogramming approaches and the effectiveness of wavelet transformations compared to traditional techniques. The study includes a novel dataset with ground truth, utilizing diverse music genres. Results indicate that while performance varies with band choice, image processing strategies can successfully enhance audio retrieval capabilities.

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Bridging Image and Audio Retrieval Techniques: A Study on Classification and Processing

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  1. Audio Meets Image Retrieval Techniques Dave Kauchak Department of Computer Science University of California, San Diego dkauchak@cs.ucsd.edu

  2. Image vs. Audio ? ? ? ? ? ? Rock Classical Country

  3. Image techniques to audio • Idea: Apply image retrieval (and classification) techniques to audio • Image is 2-D • Audio is 1-D 

  4. Benefits • Don’t have to reinvent the wheel • Image techniques have had fairly good success • More literature in image processing • Audio retrieval is a relatively new field

  5. Key Concepts and Goals • Image techniques to audio processing • Apply a number of different image techniques (and show they work ) • Relate various parts of audio to counterparts in image • Novel data set with known ground truth • Multiple input for audio • Raw audio

  6. A first step… • Audio retrieval • Input: A number of songs • Output: “Similar” songs from an audio database • Histogramming methods (Puzicha et. al.) • Wavelets instead of gabor filters

  7. Basic Technique histogram Database DWT Most “similar” songs

  8. Normal vs. Proportional Histogramming • Remember DWT: • Different number of samples per level • Normal: Histogram each level with same number of bins • Proportional: Histogram each level keeping samples/bin equal

  9. Compare Histograms • Chi-square on each level • Sum chi-square value and use for dissimilarity measure (lower the better) • Sum dissimilarity over all input songs

  10. Ground Truth Data Set • Songs by 4 different bands (10 songs each) • Dave Mathews band • U2 • Blink 182 • Green Day • Mono, sampled at 22 KHz from a number of sources

  11. Experiment • Input = 5 songs by a single band • Goal = Pull out 5 other songs by that band • 10 random experiments per band (40 total) • Normal bins: 8, 16, 32, 64, 128, 192, 256, 320, 384, 448, 512 • Proportional bins: 4, 8, 16, 32, 64

  12. Scoring • By points: • 5 pts. Correct answer in first place • 4 pts. Correct answer in second place, etc. • Perfect = 5+4+3+2+1 = 15 • Percentage correct at each place • Percentage that have correct answer less than or equal to place

  13. Results: Points

  14. Results: Points Proportional

  15. Best Score Results: 16 bins 

  16. Different Bands

  17. Percentage correct

  18. One last result 

  19. Summary of Results • Overall, results are not amazing • Band choice has large influence • Normal and Proportional perform somewhat similar • Proportional is more even over all bands • Bin size doesn’t appear to be crucial • 75% of a chance a song by the same band will end up in top 5

  20. Next Step… • Adaptive Binning • Vary Parameters • Levels • Song length • Histogram comparison methods • Another image retrieval algorithm • Boosting for feature selection using large feature set? • Other? • Larger and more diverse database

  21. Conclusion • Even though results are not fabulous, image processing techniques CAN be used for audio processing • Using bands for testing allows for ground truth • Audio files are BIG!

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