Sound Event Detection in Synthetic Audio: Analysis of the DCASE 2016 Task Results

As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds. This task, which follows the `Event Detection - Office Synthetic' task of DCASE 2013, studies the behaviour of tested algorithms when facing controlled levels of audio complexity with respect to background noise and polyphony/density, with the added benefit of a very accurate ground truth. This paper presents the task formulation, evaluation metrics, submitted systems, and provides a statistical analysis of the results achieved, with respect to various aspects of the evaluation dataset

The bag-of-frames approach: a not so sufficient model for urban soundscapes

The "bag-of-frames" approach (BOF), which encodes audio signals as the long-term statistical distribution of short-term spectral features, is commonly regarded as an effective and sufficient way to represent environmental sound recordings (soundscapes) since its introduction in an influential 2007 article. The present paper describes a concep-tual replication of this seminal article using several new soundscape datasets, with results strongly questioning the adequacy of the BOF approach for the task. We show that the good accuracy originally re-ported with BOF likely result from a particularly thankful dataset with low within-class variability, and that for more realistic datasets, BOF in fact does not perform significantly better than a mere one-point av-erage of the signal's features. Soundscape modeling, therefore, may not be the closed case it was once thought to be. Progress, we ar-gue, could lie in reconsidering the problem of considering individual acoustical events within each soundscape

On the visual display of audio data using stacked graphs

Visualisation is an important tool for many steps of a research project. In this paper, we present several displays of audio data based on stacked graphs. Thanks to a careful use of the layering the proposed displays concisely convey a large amount of information. Many flavours are presented, each useful for a specific type of data, from spectral and chromatic data to multi-source and multi channel data. We shall demonstrate that such displays for the case of spectral and chromatic data offer a different compromise than the traditional spectrogram and chroma gram, emphasizing timing information over frequency

Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images

A large-scale feature selection wrapper is discussed for the classification of high dimensional remote sensing. An efficient implementation is proposed based on intrinsic properties of Gaussian mixtures models and block matrix. The criterion function is split into two parts:one that is updated to test each feature and one that needs to be updated only once per feature selection. This split saved a lot of computation for each test. The algorithm is implemented in C++ and integrated into the Orfeo Toolbox. It has been compared to other classification algorithms on two high dimension remote sensing images. Results show that the approach provides good classification accuracies with low computation time

GMM-based classification from noisy features

International audienceWe consider Gaussian mixture model (GMM)-based classification from noisy features, where the uncertainty over each feature is represented by a Gaussian distribution. For that purpose, we first propose a new GMM training and decoding criterion called log-likelihood integration which, as opposed to the conventional likelihood integration criterion, does not rely on any assumption regarding the distribution of the data. Secondly, we introduce two new Expectation Maximization (EM) algorithms for the two criteria, that allow to learn GMMs directly from noisy features. We then evaluate and compare the behaviors of two proposed algorithms with a categorization task on artificial data and speech data with additive artificial noise, assuming the uncertainty parameters are known. Experiments demonstrate the superiority of the likelihood integration criterion with the newly proposed EM learning in all tested configurations, thus giving rise to a new family of learning approaches that are insensitive to the heterogeneity of the noise characteristics between testing and training data

On the visual display of audio data using stacked graphs

Visualisation is an important tool for many steps of a research project. In this paper, we present several displays of audio data based on stacked graphs. Thanks to a careful use of the layering the proposed displays concisely convey a large amount of information. Many flavours are presented, each useful for a specific type of data, from spectral and chromatic data to multi-source and multi channel data. We shall demonstrate that such displays for the case of spectral and chromatic data offer a different compromise than the traditional spectrogram and chroma gram, emphasizing timing information over frequency

Extended playing techniques: The next milestone in musical instrument recognition

The expressive variability in producing a musical note conveys information essential to the modeling of orchestration and style. As such, it plays a crucial role in computer-assisted browsing of massive digital music corpora. Yet, although the automatic recognition of a musical instrument from the recording of a single "ordinary" note is considered a solved problem, automatic identification of instrumental playing technique (IPT) remains largely underdeveloped. We benchmark machine listening systems for query-by-example browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets of instrument, mute, and technique. We identify and discuss three necessary conditions for significantly outperforming the traditional mel-frequency cepstral coefficient (MFCC) baseline: the addition of second-order scattering coefficients to account for amplitude modulation, the incorporation of long-range temporal dependencies, and metric learning using large-margin nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7% for instrument recognition (baseline at 89.0%) and of 61.0% for IPT recognition (baseline at 44.5%). We interpret this gain through a qualitative assessment of practical usability and visualization using nonlinear dimensionality reduction.Comment: 10 pages, 9 figures. The source code to reproduce the experiments of this paper is made available at: https://www.github.com/mathieulagrange/dlfm201