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Anvita Wisp 2007 Presentation


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Anvita Wisp 2007 Presentation

  1. 1. Exploding information •Recent studies show that most of the stored data is in the form of multimedia. •Large volume of multimedia data makes it difficult to handle it manually •Need to have an 1 hr of TV broadcast across the world is 100 Petabyte. automatic method to Source: organize and use it info/summary.html#tv appropriately.
  2. 2. Audio indexing Audio classification - An Reason of choosing audio data ● important step in building an for study audio indexing system Easier to process – An audio indexing system Contains significant information – Indexing – method of ● organizing data for further search and retrieval. Example – book indexing Audio Indexing – indexing ● non-text data using audio part of it Source: J. Makhoul et. al. “Speech and language technologies for audio indexing and retrieval”, in Proc. of the IEEE, 88(8), pp. 1338-1353, 2000.
  3. 3. Levels of information in audio signal Subsegmental information ● Related to excitation source characteristics – Segmental information ● Related to system / physiological characteristics – Suprasegmental information ● Related to behavioural characteristics of audio –
  4. 4. Missing component in existing approaches and it's importance Features derived based on spectral analysis ● Carry significant properties of audio data at segmental level – Miss information present at subsegmental, suprasegmental level – Perceptually significant information in linear prediction ● (LP) residual of signal Complimentary in nature to the spectral information – Suprasegmental information not being used in current systems –
  5. 5. EXPLORING SUPRASEGMENTAL FEATURES USING LP RESIDUAL FOR AUDIO CLIP CLASSIFICATION B.Yegnanarayana Anvita Bajpai International Institute of Applied Research Group Satyam Information Technology Computer Services Ltd., Hyderabad Bangalore
  6. 6. Audio clip classification Closed set problem ● To classify a given audio clip in one of the following ● predefined categories Advertisement, Cartoon, Cricket, Football, News – Issues in audio clip classification ● Feature extraction – Effective representation of data to capture all significant properties of audio for ● the task Robust under various conditions ● Classification – Formulation of a distance measure and rule/models ● Training a models for the task – Testing – actual classification task – Combining evidences from different systems –
  7. 7. Presence of audio-specific Residual Original information in LP residual Aa_res.wav Aa1.wav Aa1.wav
  8. 8. uprasegmental information in Hilbert nvelope of LP residual of audio signal
  9. 9. Suprasegmental information in LP residual for audio clip classification Autocorrelation samples of Hilbert envelope of LP residual for 5 audio classes
  10. 10. Statistics of autocorrelation sequence Correction – here we have statistics of autocorrelation sequence peaks of HE (not LP residual)
  11. 11. Statistics of autocorrelation sequence
  12. 12. Summary & Conclusions Need to organize multimedia data because of its ● large volume and need in real-life applications Shown presence of audio-specific ● suprasegmental information in LP residual Need to explore methods to use the ● suprasegmental information as an additional evidence for the audio clip classification task