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Anvita Ncvpripg 2008 Presentation


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Anvita Ncvpripg 2008 Presentation

  1. 1. USE OF SUPRASEGMENTAL FEATURES PRESENT IN LP RESIDUAL FOR AUDIO CLIP CLASSIFICATION - Anvita Bajpai Applied Research Group Satyam Computer Services Ltd. Bangalore
  2. 2. 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. 1
  3. 3. 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. 2
  4. 4. 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 – 5
  5. 5. 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 – 3
  6. 6. 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 – 4
  7. 7. Presence of audio-specific Residual Original information in LP residual Aa_res.wav Aa1.wav Aa1.wav 6
  8. 8. uprasegmental information in Hilbert nvelope of LP residual of audio signal 7
  9. 9. Suprasegmental information in LP residual for audio clip classification 8 Autocorrelation samples of Hilbert envelope of LP residual for 5 audio classes
  10. 10. Statistics of autocorrelation sequence 9 Correction – here we have statistics of autocorrelation sequence peaks of HE (not LP residual)
  11. 11. Classification results based on suprasegmental features using SVM # of clips correctly classified Audio Class (out of 20 clips for each class) Advertisement 11 Cartoon 19 Cricket 16 Football 04 News 10 11
  12. 12. Conclusions and future work 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, and its Hilbert envelope Statistics of autocorrelation sequence of Hilbert envelop is shown ● to enhance these features Demonstrated the use of SVM to classify audio based on variance ● of autocorrelation sequence of Hilbert envelop Need to extend the framework for other audio indexing ● applications Need to explore methods to combine the suprasegmental ● information to the systems based on segmental and subsegmental features, for the audio clip classification task (though little far..) Building a multimedia indexing system ● 12
  13. 13. Publications Anvita Bajpai and B. Yegnanarayana, “Exploring Suprasegmental Features using LP Residual 1. for Audio Clip Classification”, Workshop on Image and Signal Processing (WISP-2007), IIT Guwahati, India, 28-29 December 2007. Anvita Bajpai, “HTB Security Administration using UMX”, An Oracle Technical White Paper, 2. March 2006 Anvita Bajpai and B. Yegnanarayana, “Audio Clip Classification using LP Residual and Neural 3. Networks Models”, European Signal and Image Processing Conference (EUSIPCO-2004), Vienna, Austria, 6-10 September 2004 Anvita Bajpai and B. Yegnanarayana, “Exploring Features for Audio Indexing using LP Residual 4. and AANN Models”, accepted for The 17th International FLAIRS Conference (FLAIRS - 2004), Miami Beach, Florida, 17-19 May 2004. Anvita Bajpai and B. Yegnanarayana, “Exploring Features for Audio Clip Classification using 5. LP Residual and Neural Networks Models”, International Conference on Intelligent Signal and Image Processing (ICISIP-2004), Chennai, India, 4-7 January 2004 Gaurav Aggarwal, Anvita Bajpai and B. Yegnanarayana, “Exploring Features for Audio 6. Indexing”, in Indian Research Scholar Seminar (IRIS-2002), Indian Institute of Science, Bangalore, India, March 2002 Anvita Bajpai, “State of the art in Web Design and Content Creation for Indian Languages”, in 7. National level Workshop on Translation Support Systems (STRANS-2001) IIT Kanpur, February 2001 Anvita Bajpai, “Web Developmental Issues – A Case Study of GITASUPERSITE”, in National 8. level Workshop on Building Large Websites, IIT Kanpur, November 2000