Challenge results and analysis of submitted systems have been published in:

@article{Mesaros2018_TASLP,
    author = "Mesaros, A. and Heittola, T. and Benetos, E. and Foster, P. and Lagrange, M. and Virtanen, T. and Plumbley, M. D.",
    journal = "IEEE/ACM Transactions on Audio, Speech, and Language Processing",
    title = "Detection and Classification of Acoustic Scenes and Events: Outcome of the {DCASE} 2016 Challenge",
    year = "2018",
    volume = "26",
    number = "2",
    pages = "379--393",
    abstract = "Public evaluation campaigns and datasets promote active development in target research areas, allowing direct comparison of algorithms. The second edition of the challenge on detection and classification of acoustic scenes and events (DCASE 2016) has offered such an opportunity for development of the state-of-the-art methods, and succeeded in drawing together a large number of participants from academic and industrial backgrounds. In this paper, we report on the tasks and outcomes of the DCASE 2016 challenge. The challenge comprised four tasks: acoustic scene classification, sound event detection in synthetic audio, sound event detection in real-life audio, and domestic audio tagging. We present each task in detail and analyze the submitted systems in terms of design and performance. We observe the emergence of deep learning as the most popular classification method, replacing the traditional approaches based on Gaussian mixture models and support vector machines. By contrast, feature representations have not changed substantially throughout the years, as mel frequency-based representations predominate in all tasks. The datasets created for and used in DCASE 2016 are publicly available and are a valuable resource for further research.",
    keywords = "Acoustics;Event detection;Hidden Markov models;Speech;Speech processing;Tagging;Acoustic scene classification;audio datasets;pattern recognition;sound event detection",
    doi = "10.1109/TASLP.2017.2778423",
    issn = "2329-9290",
    month = "Feb"
}

Results for each tasks are presented in task specific results pages:

System outputs:

DCASE2016 Challenge Submissions Package (28.7 MB)

Introduction

Sounds carry a large amount of information about our everyday environment and physical events that take place in it. We can perceive the sound scene we are within (busy street, office, etc.), and recognize individual sound sources (car passing by, footsteps, etc.). Developing signal processing methods to automatically extract this information has huge potential in several applications, for example searching for multimedia based on its audio content, making context-aware mobile devices, robots, cars etc., and intelligent monitoring systems to recognize activities in their environments using acoustic information. However, a significant amount of research is still needed to reliably recognize sound scenes and individual sound sources in realistic soundscapes, where multiple sounds are present, often simultaneously, and distorted by the environment.

Previous challenges

Public evaluations are common in many areas of research, with some challenges being active for many consecutive years. They help push the boundaries of algorithm developments to deal with more and more complex tasks. TRECVID Multimedia Event detection is another of the long tradition evaluations, with focus on audiovisual, multi-modal event detection in video recordings. Such public evaluations provide a good opportunity for code dissemination, unification and definition of terms, procedures, benchmark datasets and evaluation metrics. It is our wish to provide a similar tool for computational auditory scene analysis, specifically for detection and classification of sound scenes and events.

The previously organized DCASE2013 challenge (sponsored by the IEEE AASP TC, and held at WASPAA 2013) attracted the interest of the research community and had a good participation rate. It also contributed on creating benchmark datasets and fostered reproducible research (6 out of 18 participating teams had their source code released through the challenge). Based on its success, we propose to organize the follow-up challenge on the performance evaluation of systems for the detection and classification of sound events. This challenge will move the DCASE setup closer to real world applications, by providing more complex problems. This will help defining a common ground for researchers that actively pursue research on this field, and offer a reference point for systems developed to perform parts of this task.

Tasks

Continuing the tasks of the previous DCASE, the proposed tasks for the challenge are acoustic scene classification and sound event detection within a scene.

Challenge setup

For each challenge task, a development dataset and baseline system will be provided. Challenge evaluation will be done using an evaluation dataset that will be published shortly before the deadline. Task-specific rules are available on the task pages.

General challenge rules