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Future Perspective

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Computational Analysis of Sound Scenes and Events

Abstract

This book has covered the underlying principles and technologies of sound recognition, and described several current application areas. However, the field is still very young; this chapter briefly outlines several emerging areas, particularly relating to the provision of the very large training sets that can be exploited by deep learning approaches. We also forecast some of the technological and application advances we expect in the short-to-medium future.

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References

  1. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems, pp. 577–584 (2003)

    Google Scholar 

  2. Auer, P., Ortner, R.: A boosting approach to multiple instance learning. In: European Conference on Machine Learning, pp. 63–74. Springer, Berlin (2004)

    Google Scholar 

  3. Aytar, Y., Vondrick, C., Torralba, A.: Soundnet: learning sound representations from unlabeled video. In: Advances in Neural Information Processing Systems, pp. 892–900 (2016)

    Google Scholar 

  4. Babenko, B.: Multiple instance learning: algorithms and applications. Technical Report, Department of Computer Science and Engineering, University of California, San Diego (2008)

    Google Scholar 

  5. Bandyopadhyay, S., Ghosh, D., Mitra, R., Zhao, Z.: MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Sci. Rep. 5, 8004 (2015)

    Article  Google Scholar 

  6. Briggs, F., Lakshminarayanan, B., Neal, L., Fern, X.Z., Raich, R., Hadley, S.J., Hadley, A.S., Betts, M.G.: Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach. J. Acoust. Soc. Am. 131(6), 4640–4650 (2012)

    Article  Google Scholar 

  7. Büchler, M., Allegro, S., Launer, S., Dillier, N.: Sound classification in hearing aids inspired by auditory scene analysis. EURASIP J. Adv. Signal Process. 2005(18), 387845 (2005)

    Article  MATH  Google Scholar 

  8. Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active learning with statistical models. J. Artif. Intell. Res. 4(1), 129–145 (1996)

    MATH  Google Scholar 

  9. Cooke, M., Ellis, D.P.: The auditory organization of speech and other sources in listeners and computational models. Speech Commun. 35(3), 141–177 (2001)

    Article  MATH  Google Scholar 

  10. Correia, J., Trancoso, I., Raj, B.: Adaptation of SVM for MIL for inferring the polarity of movies and movie reviews. In: Spoken Language Technology Workshop (SLT), 2016 IEEE, pp. 258–264. IEEE, New York (2016)

    Google Scholar 

  11. Dalvi, B., Callan, J., Cohen, W.W.: Entity list completion using set expansion techniques. In: Proceedings of the Nineteenth Text REtrieval Conference (TREC 2010). NIST, Gaithersburg MD (2011)

    Google Scholar 

  12. Doppler Labs: HearOne wireless smart earbuds (2017). http://hereplus.me

    Google Scholar 

  13. Elizalde, B., Raj, B., Vincent, E.: Large-scale weakly supervised sound event detection for smart cars (2017). http://www.cs.tut.fi/sgn/arg/dcase2017/challenge/task-large-scale-sound-event-detection

    Google Scholar 

  14. Frey, B.J., Deng, L., Acero, A., Kristjansson, T.T.: ALGONQUIN: iterating laplace’s method to remove multiple types of acoustic distortion for robust speech recognition. In: INTERSPEECH, pp. 901–904 (2001)

    Google Scholar 

  15. Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: an ontology and human-labeled dataset for audio events. In: IEEE ICASSP 2017, New Orleans (2017). https://research.google.com/pubs/pub45857.html

  16. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics, vol. 2, pp. 539–545. Association for Computational Linguistics, Stroudsburg, PA (1992)

    Google Scholar 

  17. Hershey, S., Chaudhury, S., Ellis, D.P.W., Gemmeke, J., Jansen, A., Moore, R.C., Plakal, M., Sauros, R.A., Seybold, B., Slaney, M., Weiss, R.: CNN architectures for large-scale audio classification. In: IEEE ICASSP 2017, New Orleans (2017). https://research.google.com/pubs/pub45611.html

  18. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  19. Jansen, A., Gemmeke, J.F., Ellis, D.P.W., Liu, X., Lawrence, W., Freedman, D.: Large-scale audio event discovery in one million youtube videos. In: IEEE ICASSP 2017, New Orleans (2017)

    Google Scholar 

  20. Kingsbury, B.E., Morgan, N., Greenberg, S.: Robust speech recognition using the modulation spectrogram. Speech Commun. 25(1), 117–132 (1998)

    Article  Google Scholar 

  21. Klapuri, A.: Multiple fundamental frequency estimation by summing harmonic amplitudes. In: ISMIR, pp. 216–221 (2006)

    Google Scholar 

  22. Kong, Q., Xu, Y., Wang, W., Plumbley, M.D.: A joint detection-classification model for audio tagging of weakly labelled data. CoRR abs/1610.01797 (2016). http://arxiv.org/abs/1610.01797

  23. Kotzias, D., Denil, M., De Freitas, N., Smyth, P.: From group to individual labels using deep features. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 597–606. ACM, New York (2015)

    Google Scholar 

  24. Kumar, A., Raj, B.: Audio event detection using weakly labeled data. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 1038–1047. ACM, New York (2016)

    Google Scholar 

  25. Kumar, A., Raj, B.: Weakly supervised scalable audio content analysis. In: 2016 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE, New York (2016)

    Google Scholar 

  26. Kumar, A., Raj, B., Nakashole, N.: Discovering sound concepts and acoustic relations in text. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, New York (2017)

    Google Scholar 

  27. Leistner, C., Saffari, A., Bischof, H.: Miforests: multiple-instance learning with randomized trees. In: Computer Vision–ECCV 2010, pp. 29–42 (2010)

    Google Scholar 

  28. Mandel, M.I., Ellis, D.P.: Multiple-instance learning for music information retrieval. In: ISMIR, pp. 577–582 (2008)

    Google Scholar 

  29. Maron, O., Ratan, A.L.: Multiple-instance learning for natural scene classification. In: ICML, vol. 98, pp. 341–349 (1998)

    Google Scholar 

  30. Mesaros, A., Heittola, T., Virtanen, T.: Tut database for acoustic scene classification and sound event detection. In: Signal Processing Conference (EUSIPCO), 2016 24th European, pp. 1128–1132. IEEE, New York (2016). http://www.cs.tut.fi/~mesaros/pubs/mesaros_eusipco2016-dcase.pdf

  31. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  32. Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Betteridge, J., Carlson, A., Dalvi, B., Gardner, M., Kisiel, B., Krishnamurthy, J., Lao, N., Mazaitis, K., Mohamed, T., Nakashole, N., Platanios, E., Ritter, A., Samadi, M., Settles, B., Wang, R., Wijaya, D., Gupta, A., Chen, X., Saparov, A., Greaves, M., Welling, J.: Never-ending learning. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15) (2015)

    Google Scholar 

  33. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  34. Papadopoulos, D.P., Uijlings, J.R., Keller, F., Ferrari, V.: Training object class detectors with click supervision. In: Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR). Honolulu, Hawaii (2017). ArXiv preprint arXiv:1704.06189

    Google Scholar 

  35. Pillai, R., Qazi, U.W.: Acoustic analysis of text (aat): Extracting sound out of words. QSIURP Research Report, Carnegie Mellon University Qatar (2016)

    Google Scholar 

  36. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  37. Sager, S., Borth, D., Elizalde, B., Schulze, C., Raj, B., Lane, I., Dengel, A.: AudioSentiBank: large-scale semantic ontology of acoustic concepts for audio content analysis. arXiv preprint (arXiv:1607.03766) (2016)

    Google Scholar 

  38. Salamon, J., Jacoby, C., Bello, J.P.: A dataset and taxonomy for urban sound research. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 1041–1044. ACM, New York (2014). https://serv.cusp.nyu.edu/projects/urbansounddataset/salamon_urbansound_acmmm14.pdf

  39. Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)

    Google Scholar 

  40. Stowell, D., Giannoulis, D., Benetos, E., Lagrange, M., Plumbley, M.D.: Detection and classification of acoustic scenes and events. IEEE Trans. Multimedia 17(10), 1733–1746 (2015)

    Article  Google Scholar 

  41. Temko, A., Malkin, R., Zieger, C., Macho, D., Nadeu, C., Omologo, M.: Clear evaluation of acoustic event detection and classification systems. In: International Evaluation Workshop on Classification of Events, Activities and Relationships, pp. 311–322. Springer, New York (2006)

    Google Scholar 

  42. Wang, D., Brown, G.J.: Computational Auditory Scene Analysis: Principles, Algorithms, and Applications. Wiley/IEEE Press, New York (2006)

    Book  Google Scholar 

  43. Wikipedia: Amazon Echo (2017). https://en.wikipedia.org/wiki/Amazon_Echo

  44. Xu, Y., Kong, Q., Huang, Q., Wang, W., Plumbley, M.D.: Attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging. CoRR abs/1703.06052 (2017). http://arxiv.org/abs/1703.06052

  45. Zhao, S., Heittola, T., Virtanen, T.: Active learning for sound event classification by clustering unlabeled data. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (2017)

    Google Scholar 

  46. Zhao, Z., Fu, G., Liu, S., Elokely, K.M., Doerksen, R.J., Chen, Y., Wilkins, D.E.: Drug activity prediction using multiple-instance learning via joint instance and feature selection. BMC Bioinf. 14(14), S16 (2013)

    Article  Google Scholar 

  47. Zhou, Z.H., Zhang, M.L.: Neural networks for multi-instance learning. In: Proceedings of the International Conference on Intelligent Information Technology, Beijing, pp. 455–459 (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Google Inc, 111 8th Ave, New York, NY, 10027, USA

    Dan Ellis

  2. Laboratory of Signal Processing, Tampere University of Technology, Tampere, Finland

    Tuomas Virtanen

  3. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    Mark D. Plumbley

  4. Carnegie Mellon University, Pittsburgh, PA, USA

    Bhiksha Raj

Authors
  1. Dan Ellis
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  2. Tuomas Virtanen
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  3. Mark D. Plumbley
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  4. Bhiksha Raj
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Corresponding author

Correspondence to Tuomas Virtanen .

Editor information

Editors and Affiliations

  1. Laboratory of Signal Processing, Tampere University of Technology, Tampere, Finland

    Tuomas Virtanen

  2. Centre for Vision, Speech and Signal Processing, University of Surrey, Surrey, United Kingdom

    Mark D. Plumbley

  3. Google Inc., New York, New York, USA

    Dan Ellis

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Ellis, D., Virtanen, T., Plumbley, M.D., Raj, B. (2018). Future Perspective. In: Virtanen, T., Plumbley, M., Ellis, D. (eds) Computational Analysis of Sound Scenes and Events. Springer, Cham. https://doi.org/10.1007/978-3-319-63450-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-63450-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63449-4

  • Online ISBN: 978-3-319-63450-0

  • eBook Packages: EngineeringEngineering (R0)

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