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Environmental sound recognition using short-time feature aggregation


Recognition of environmental sound is usually based on two main architectures, depending on whether the model is trained with frame-level features or with aggregated descriptions of acoustic scenes or events. The former architecture is appropriate for applications where target categories are known in advance, while the later affords a less supervised approach. In this paper, we propose a framework for environmental sound recognition based on blind segmentation and feature aggregation. We describe a new set of descriptors, based on Recurrence Quantification Analysis (RQA), which can be extracted from the similarity matrix of a time series of audio descriptors. We analyze their usefulness for recognition of acoustic scenes and events in addition to standard feature aggregation. Our results show the potential of non-linear time series analysis techniques for dealing with environmental sounds.

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  1. Note that our original submission to the D-CASE challenge obtained very good results by narrowing the frequency range to below 1 Khz. However in our experiments this only worked for the challenge dataset, so for the sake of generality here we chose a wider range.


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The first author was with the Music Technology Group at Universitat Pompeu Fabra for the main part of this work. The third author was with the Music Technology Group at Universitat Pompeu Fabra for part of this work. This work has been suported by the DFG cluster of excellence EXC 1077/1 “Hearing4all”.

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Correspondence to Gerard Roma.

Appendix: RQA features

Appendix: RQA features

This section details the equations used for computing this features, mostly derived from Webber & Zbilut (1994). Here, R refers to recurrence plot as described in Section 2.4.

  • Recurrence rate (REC) is just the percentage of points in the recurrence plot.

    $$ REC = (1/N^{2}) \sum\limits_{i,j=1}^{N}{R_{i,j}} $$
  • Determinism (DET) is measured as the percentage of points that are in diagonal lines.

    $$ DET = {{\sum}_{l=l_{min}}^{N}{lP(l)} \over{ {\sum}_{i,j=1}^{N}{R_{i,j}}} } $$

    where P(l) is the histogram of diagonal line lengths l.

  • Laminarity (LAM) is the percentage of points that form vertical lines.

    $$ LAM = {{\sum}_{v=v_{min}}^{N}{vP(v)} \over{ {\sum}_{v=1}^{N}{vP(v)} } } $$

    where P(v) is the histogram of vertical line lengths v.

  • The ratio between DET and REC is often used. We also use the ratio between LAM and REC, so we define them as

    $$ DRATIO =N^{2} {{\sum}_{l=l_{min}}^{N}{lP(l)} \over{ ({\sum}_{l=1}^{N}{lP(l)})^{2}}} $$
    $$ VRATIO =N^{2} {{\sum}_{v=v_{min}}^{N}{vP(v)} \over{ ({\sum}_{v=1}^{N}{vP(v)})^{2} }} $$
  • LEN and Trapping Time TT are the average diagonal and vertical line lengths.

    $$ LEN = {{\sum}_{l=l_{min}}^{N}{lP(l)} \over{ {\sum}_{l=l_{min}}^{N}{P(l)}}} $$
    $$ TT = {{\sum}_{v=v_{min}}^{N}{vP(v)} \over{ {\sum}_{v=v_{min}}^{N}{P(v)}}} $$
  • Another common feature is the length of the longest diagonal and vertical lines. The inverse of the maximum diagonal (called Divergence) is also used. We use the inverse of both vertical and diagonal maximum lengths.

    $$ DDIV = {1\over{max(l)}} $$
    $$ VDIV = {1\over{max(v)}} $$
  • Finally, the Shannon entropy of the diagonal line lengths is commonly used. We also compute the entropy for vertical line lengths.

    $$ DENT = - \sum\limits_{l=l_{min}}^{N}{P(l)ln(P(l))} $$
    $$ VENT = - \sum\limits_{v=v_{min}}^{N}{P(v)ln(P(v))} $$

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Roma, G., Herrera, P. & Nogueira, W. Environmental sound recognition using short-time feature aggregation. J Intell Inf Syst 51, 457–475 (2018).

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  • Audio databases
  • Event detection
  • Environmental sound recognition
  • Audio features
  • Recurrence quantification analysis
  • Pattern recognition