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DESED: A New Dataset for Sound Event Detection and a Comparative Evaluation of SOTA Systems

4/5/2020

We present a comparative analysis of the performance of state-of-the-art sound event detection systems. In particular, we study the robustness of the systems to noise and signal degradation, which is known to impact model generalization. Our analysis is based on the results of task 4 of the DCASE 2019 challenge, where submitted systems were evaluated on, in addition to real-world recordings, a series of synthetic soundscapes that allow us to carefully control for different soundscape characteristics. Our results show that while overall systems exhibit significant improvements compared to previous work, they still suffer from biases that could prevent them from generalizing to real-world scenarios.

New Dataset: DESED (Domestic Environment Sound Event Detection)

The study was performed using a new dataset for sound event detection, DESED:

Dataset website: https://project.inria.fr/desed/
Includes both recorded and synthesized soundscapes
Includes isolated sound events allowing users to generate new data using Scaper

Sound Event Detection in Synthetic Domestic Environments
R. Serizel, N. Turpault, A. Shah, J. Salamon
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.
[IEEE][PDF][BibTeX][Copyright]

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Chirping up the Right Tree: Incorporating Biological Taxonomies into Deep Bioacoustic Classifiers

3/5/2020

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Class imbalance in the training data hinders the generalization ability of machine listening systems. In the context of bioacoustics, this issue may be circumvented by aggregating species labels into super-groups of higher taxonomic rank: genus, family, order, and so forth. However, different applications of machine listening to wildlife monitoring may require different levels of granularity. This paper introduces TaxoNet, a deep neural network for structured classification of signals from living organisms. TaxoNet is trained as a multitask and multilabel model, following a new architectural principle in end-to-end learning named "hierarchical composition": shallow layers extract a shared representation to predict a root taxon, while deeper layers specialize recursively to lower-rank taxa. In this way, TaxoNet is capable of handling taxonomic uncertainty, out-of-vocabulary labels, and open-set deployment settings. An experimental benchmark on two new bioacoustic datasets (ANAFCC and BirdVox-14SD) leads to state-of-the-art results in bird species classification. Furthermore, on a task of coarse-grained classification, TaxoNet also outperforms a flat single-task model trained on aggregate labels.

Try it out: pip install birdvoxdetect (https://github.com/BirdVox/birdvoxdetect)

Chirping up the Right Tree: Incorporating Biological Taxonomies into Deep Bioacoustic Classifiers
J. Cramer, V. Lostanlen, A. Farnsworth, J. Salamon, J.P. Bello
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.
[IEEE][PDF][BibTeX][Copyright]

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Few-Shot Sound Event Detection

3/5/2020

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Locating perceptually similar sound events within a continuous recording is a common task for various audio applications. However, current tools require users to manually listen to and label all the locations of the sound events of interest, which is tedious and time-consuming. In this work, we (1) adapt state-of-the-art metric-based few-shot learning methods to automate the detection of similar-sounding events, requiring only one or few examples of the target event, (2) develop a method to automatically construct a partial set of labeled examples (negative samples) to reduce user labeling effort, and (3) develop an inference-time data augmentation method to increase detection accuracy. To validate our approach, we perform extensive comparative analysis of few-shot learning methods for the task of keyword detection in speech. We show that our approach successfully adapts closed-set few-shot learning approaches to an open-set sound event detection problem.

Few-Shot Sound Event Detection
Y. Wang, J. Salamon, N.J. Bryan and J.P. Bello
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.
[IEEE][PDF][BibTeX][Copyright]

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Disentangled Multidimensional Metric Learning for Music Similarity

3/5/2020

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Music similarity search is useful for a variety of creative tasks such as replacing one music recording with another recording with a similar "feel", a common task in video editing. For this task, it is typically necessary to define a similarity metric to compare one recording to another. Music similarity, however, is hard to define and depends on multiple simultaneous notions of similarity (i.e. genre, mood, instrument, tempo). While prior work ignore this issue, we embrace this idea and introduce the concept of multidimensional similarity and unify both global and specialized similarity metrics into a single, semantically disentangled multidimensional similarity metric. To do so, we adapt a variant of deep metric learning called conditional similarity networks to the audio domain and extend it using track-based information to control the specificity of our model. We evaluate our method and show that our single, multidimensional model outperforms both specialized similarity spaces and alternative baselines. We also run a user-study and show that our approach is favored by human annotators as well.

Disentangled Multidimensional Metric Learning for Music Similarity
J. Lee, N.J. Bryan, J. Salamon, Z. Jin, J. Nam
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.
[IEEE][PDF][BibTeX][Copyright]

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DESED: A New Dataset for Sound Event Detection and a Comparative Evaluation of SOTA Systems

New Dataset: DESED (Domestic Environment Sound Event Detection)

Chirping up the Right Tree: Incorporating Biological Taxonomies into Deep Bioacoustic Classifiers

Few-Shot Sound Event Detection

Disentangled Multidimensional Metric Learning for Music Similarity

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