Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge

Abstract

Public evaluation campaigns and datasets promote\ud active development in target research areas, allowing direct\ud comparison of algorithms. The second edition of the challenge\ud on Detection and Classification of Acoustic Scenes and Events\ud (DCASE 2016) has offered such an opportunity for development\ud of state-of-the-art methods, and succeeded in drawing together a\ud large number of participants from academic and industrial backgrounds.\ud In this paper, we report on the tasks and outcomes of\ud the DCASE 2016 challenge. The challenge comprised four tasks:\ud acoustic scene classification, sound event detection in synthetic\ud audio, sound event detection in real-life audio, and domestic\ud audio tagging. We present in detail each task and analyse the\ud submitted systems in terms of design and performance. We\ud observe the emergence of deep learning as the most popular\ud classification method, replacing the traditional approaches based\ud on Gaussian mixture models and support vector machines. By\ud contrast, feature representations have not changed substantially\ud throughout the years, as mel frequency-based representations\ud predominate in all tasks. The datasets created for and used in\ud DCASE 2016 are publicly available and are a valuable resource\ud for further research

ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection

“…An unsupervised approach is often adopted for ADMOS systems [10][11][12][13][14][15] because it is difficult to build an extensive set of anomalous sounds in the real world. Therefore, a DNN is trained by using only given normal sound and anomalous sound is defined as "unknown" sounds, in contrast to supervised DCASE challenge tasks [21,22] for detecting "defined" anomalous sounds such as gunshots [4]. This definition results in misdetection caused by both a rare normal sound and the difference between the recording condition in training/test dataset.…”

MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

“…Another approach is to detect anomalies from sound by using technologies for acoustic scene classification and event detection [7][8][9][10][11][12][13]. Remarkable advancements have been made in the classification of acoustic scenes and the detection of acoustic events, and there are many promising state-of-the-art studies in this vein [14][15][16]. It is clear that the emergence of numerous open benchmark datasets [17][18][19][20] is essential for the advancement of the research field.…”

Towards Joint Sound Scene and Polyphonic Sound Event Recognition

“…Core problems in the field include identifying the acoustic environment of an audio stream, this is acoustic scene classification (ASC) or sound scene recognition [2], and on detecting the sound events or sound objects within a scene, namely sound event detection (SED) [3]. ASC and SED are commonly considered as two separate tasks in understanding sound scenes, as can be demonstrated by the evolution of the field through the IEEE AASP Challenges in Detection and Classification of Acoustic Scenes and Events (DCASE) [4,5,6].…”