Skip to main content
SpringerLink
Log in

AtResNet: Residual Atrous CNN with Multi-scale Feature Representation for Low Complexity Acoustic Scene Classification

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Acoustic Scene Classification (ASC) aims to categorize real-world audio into one of the predetermined classes that identifies the recording environment of the audio. State-of-the-art ASC algorithms have excellent performance in terms of accuracy due to the emergence of deep learning algorithms. In particular, Convolutional Neural Networks (CNN) have set a new benchmark in ASC due to their promising performance. Despite the emergence of new frameworks, the interest in ASC is growing progressively with a shift of focus from enhancing accuracy to reducing model complexity. In this work, we introduce the AtResNet, a residual atrous CNN for low complexity acoustic scene classification. The AtResNet utilizes dilated convolutions and residual connections to reduce the number of model parameters. To further enhance the performance of AtResNet, we introduce a multi-scale feature representation method called multi-scale mel spectrogram (ms2). To compute the ms2, we evaluate the mel spectrogram on the wavelet subbands of the signal. We assessed AtResNet with ms2 on three benchmark datasets in ASC. The results suggest that our method significantly outperformed the CNN-based techniques in addition to a baseline system based on log mel spectrum for signal representation. AtResNet offers a 28.73% reduction in the model parameters against a baseline CNN. Furthermore, the AtResNet has a model size of 81 KB with post-training quantization of network weights. It makes AtResNet suitable for deployment in context-aware devices.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability Statement

The authors confirm that the audio data (referred to as DCASE18, DCASE19, and DCASE20 in the manuscript) that support the findings of this study are available in Zenodo with the identifiers https://doi.org/10.5281/zenodo.1228142, https://doi.org/10.5281/zenodo.2589280, and https://doi.org/10.5281/zenodo.3819968.

References

  1. G. Beylkin, R. Coifman, V. Rokhlin, Fast wavelet transforms and numerical algorithms I. Commun. Pure Appl. Math. 44(2), 141–183 (1991). https://doi.org/10.1002/cpa.3160440202

    Article  MathSciNet  MATH  Google Scholar 

  2. V. Bisot, S. Essid, G. Richard, in 23rd European Signal Processing Conference, EUSIPCO 2015 (IEEE, Nice, France, 2015), pp. 719–723. https://doi.org/10.1109/EUSIPCO.2015.7362477

  3. H. Chen, Z. Liu, Z. Liu, P. Zhang, Long-term scalogram integrated with an iterative data augmentation scheme for acoustic scene classification. J. Acoust. Soc. Am. 149(6), 4198–4213 (2021). https://doi.org/10.1121/10.0005202

    Article  Google Scholar 

  4. W.H. Choi, S.I. Kim, M.S. Keum, W. Han, H. Ko, D.K. Han, in 2011 IEEE International Conference on Consumer Electronics (ICCE) (2011), pp. 627–628. https://doi.org/10.1109/ICCE.2011.5722777

  5. F. Chollet, et al. Keras (2015). https://github.com/fchollet/keras

  6. S. Chu, S. Narayanan, C.J. Kuo, M.J. Mataric, in Proceedings of the 2006 IEEE International Conference on Multimedia and Expo (2006), pp. 885–888. https://doi.org/10.1109/ICME.2006.262661

  7. B. Clarkson, N. Sawhney, A. Pentl, in Proceedings of the International Workshop on Perceptual User Interfaces, 1998 (San Francisco, USA, 1998), pp. 4–6

  8. I. Daubechies, Ten Lectures on Wavelets (Society for Industrial and Applied Mathematics, USA, 1992)

  9. DCASE. Dcase2019 challenge, in IEEE AASP challenge on detection and classification of acoustic scenes and events. http://dcase.community/challenge2019/ (2019). Accessed: 26 June 2021

  10. K. El-Maleh, A. Samouelian, P. Kabal, in Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), vol. 1 (1999), pp. 237–240. https://doi.org/10.1109/ICASSP.1999.758106

  11. C.B. et al. SoX: Sound eXchange. http://sox.sourceforge.net/sox.html

  12. P. Gaunard, C.G. Mubikangiey, C. Couvreur, V. Fontaine, in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ’98, May 12-15, 1998 (IEEE, Seattle, Washington, USA,, 1998), pp. 3609–3612. https://doi.org/10.1109/ICASSP.1998.679661

  13. J.T. Geiger, B. Schuller, G. Rigoll, in 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (2013), pp. 1–4. https://doi.org/10.1109/WASPAA.2013.6701857

  14. P. Guyot, J. Pinquier, R. André-Obrecht, in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013 (IEEE, Vancouver, BC, Canada, 2013), pp. 793–797. https://doi.org/10.1109/ICASSP.2013.6637757

  15. Y. Han, J. Park, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017) (2017), pp. 46–50

  16. A. Harma, M. McKinney, J. Skowronek, in 2005 IEEE International Conference on Multimedia and Expo (2005), pp. 634–637. https://doi.org/10.1109/ICME.2005.1521503

  17. T. Heittola, A. Mesaros, T. Virtanen, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020) (2020). arXiv:2005.14623. Submitted

  18. H. Hu, C.H. Yang, X. Xia, X. Bai, X. Tang, Y. Wang, S. Niu, L. Chai, J. Li, H. Zhu, F. Bao, Y. Zhao, S.M. Siniscalchi, Y. Wang, J. Du, C. Lee, Device-robust acoustic scene classification based on two-stage categorization and data augmentation. CoRR (2020). arXiv:2007.08389

  19. S. Hyeji, P. Jihwan, Acoustic Scene Classification Using Various Pre-processed Features and Convolutional Neural Networks. Technical reports, DCASE2019 Challenge (2019)

  20. H. Jiang, J. Bai, S. Zhang, B. Xu, in 2005 International Conference on Natural Language Processing and Knowledge Engineering (2005), pp. 131–136. https://doi.org/10.1109/NLPKE.2005.1598721

  21. H. Jleed, M. Bouchard, in 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) (2017), pp. 1–4. https://doi.org/10.1109/CCECE.2017.7946646

  22. A. Karoui, R. Vaillancourt, Families of biorthogonal wavelets. Comput. Math. Appl. 28(4), 25–39 (1994). https://doi.org/10.1016/0898-1221(94)00124-3

    Article  MathSciNet  MATH  Google Scholar 

  23. B. Kim, S. Yang, J. Kim, S. Chang, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021) (Barcelona, Spain, 2021), pp. 21–25

  24. C. Landone, J. Harrop, J. Reiss, in Proceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007, ed. by S. Dixon, D. Bainbridge, R. Typke (Austrian Computer Society, Vienna, Austria, 2007), pp. 159–160. http://ismir2007.ismir.net/proceedings/ISMIR2007_p159_landone.pdf

  25. Y. Lee, S. Lim, I.Y. Kwak, CNN-based acoustic scene classification system. Electronics 10(4), 371 (2021). https://doi.org/10.3390/electronics10040371

    Article  Google Scholar 

  26. Y. Li, X. Li, Y. Zhang, W. Wang, M. Liu, X. Feng, Acoustic scene classification using deep audio feature and BLSTM network, in Proceedings of the 2018 International Conference on Audio, Language and Image Processing (ICALIP), pp. 371–374 (2018)

  27. T.Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollár, in 2017 IEEE International Conference on Computer Vision (ICCV) (2017), pp. 2999–3007. https://doi.org/10.1109/ICCV.2017.324

  28. L. Ma, B. Milner, D. Smith, Acoustic environment classification. ACM Trans. Speech Lang. Process. 3(2), 1–22 (2006). https://doi.org/10.1145/1149290.1149292

    Article  Google Scholar 

  29. S. Mallat, A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989). https://doi.org/10.1109/34.192463

    Article  MATH  Google Scholar 

  30. I. McLoughlin, Z. Xie, Y. Song, H. Phan, R. Palaniappan, Time-frequency feature fusion for noise robust audio event classification. Circuits Syst. Signal Process. 39, 1672–1687 (2020). https://doi.org/10.1007/s00034-019-01203-0

    Article  Google Scholar 

  31. A. Mesaros, T. Heittola, T. Virtanen, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018) (2018), pp. 9–13. https://arxiv.org/abs/1807.09840

  32. N. Moritz, J. Schröder, S. Goetze, J. Anemüller, B. Kollmeier, Acoustic Scene Classification Using Time-Delay Neural Networks and Amplitude Modulation Filter Bank Features. Technical reports, DCASE2016 Challenge (2016)

  33. R. Nandi, S. Shekhar, M. Mulimani, in Proceedings of the Interspeech 2021 (2021), pp. 561–565. https://doi.org/10.21437/Interspeech.2021-656

  34. S. Park, S. Mun, Y. Lee, H. Ko, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017) (2017), pp. 98–102

  35. L. Pham, A. Schindler, H. Tang, T. Hoang, DCASE 2021 task 1A. Technique report, DCASE2021 Challenge (2021)

  36. S.S.R. Phaye, E. Benetos, Y. Wang, in ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019), pp. 825–829. https://doi.org/10.1109/ICASSP.2019.8683288

  37. M. Plata, Deep Neural Networks with Supported Clusters Pre-classification Procedure for Acoustic Scene Recognition. Technical report, DCASE2019 Challenge (2019)

  38. R. Radhakrishnan, A. Divakaran, A. Smaragdis, in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2005. (2005), pp. 158–161. https://doi.org/10.1109/ASPAA.2005.1540194

  39. P. Ramachandran, B. Zoph, Q.V. Le, Swish: a Self-Gated Activation Function (2017). arXiv:1710.05941

  40. J. Salamon, J.P. Bello, Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process. Lett. 24(3), 279–283 (2017). https://doi.org/10.1109/lsp.2017.2657381

    Article  Google Scholar 

  41. N. Sawhney, P. Maes, Situational awareness from environmental sounds (1998)

  42. R. Serizel, V. Bisot, S. Essid, G. Richard, in 2016 IEEE International Conference on Image Processing (ICIP) (2016), pp. 948–952. https://doi.org/10.1109/ICIP.2016.7532497

  43. J. Sharma, O. Granmo, M. Goodwin, in Interspeech 2020, 21st Annual Conference of the International Speech Communication Association, Virtual Event, ed. by H. Meng, B. Xu, T.F. Zheng (ISCA, Shanghai, China, 2020), pp. 1186–1190. https://doi.org/10.21437/Interspeech.2020-1303

  44. P.P. Vaidyanathan, Multirate Systems and Filter Banks (Prentice-Hall Inc, Hoboken, 1993)

    MATH  Google Scholar 

  45. T. Virtanen, M.D. Plumbley, D. Ellis, Introduction to Sound Scene and Event Analysis (Springer International Publishing, Cham, 2018), pp. 3–12. https://doi.org/10.1007/978-3-319-63450-0_1

  46. D. Wang, G. Brown, Computational Auditory Scene Analysis: Principles, Algorithms, and Applications (Wiley, Hoboken, 2006)

    Book  Google Scholar 

  47. H. Wang, Y. Zou, W. Wang, Specaugment++: A hidden space data augmentation method for acoustic scene classification. CoRR (2021). arXiv:2103.16858

  48. F. Yu, V. Koltun, in 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, ed. by Y. Bengio, Y. LeCun (2016). arXiv:1511.07122

  49. H. Zhang, M. Cisse, Y.N. Dauphin, D. Lopez-Paz, in International Conference on Learning Representations (2018). https://openreview.net/forum?id=r1Ddp1-Rb

  50. L. Zhang, J. Han, Z. Shi, in Interspeech 2020, 21st Annual Conference of the International Speech Communication Association, Virtual Event, Shanghai, China, 25-29 October 2020, ed. by H. Meng, B. Xu, T.F. Zheng (ISCA, 2020), pp. 1181–1185. https://doi.org/10.21437/Interspeech.2020-1151

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication, College of Engineering, Trivandrum, Kulathoor, Thiruvananthapuram, Kerala, 695016, India

    Aswathy Madhu

  2. Department of Electronics and Communication, Government Engineering College, Wayanad, Kerala, 670644, India

    K. Suresh

  3. A P J Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India

    Aswathy Madhu & K. Suresh

Authors
  1. Aswathy Madhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Suresh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aswathy Madhu.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial, general and institutional interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Madhu, A., Suresh, K. AtResNet: Residual Atrous CNN with Multi-scale Feature Representation for Low Complexity Acoustic Scene Classification. Circuits Syst Signal Process 41, 7035–7056 (2022). https://doi.org/10.1007/s00034-022-02107-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-022-02107-2

Keywords

Search

Navigation