DREGON: Dataset and Methods for UAV-Embedded Sound Source Localization

International audienceThis paper introduces DREGON, a novel publicly-available dataset that aims at pushing research in sound source localization using a microphone array embedded in an unmanned aerial vehicle (UAV). The dataset contains both clean and noisy in-flight audio recordings continuously annotated with the 3D position of the target sound source using an accurate motion capture system. In addition, various signals of interests are available such as the rotational speed of individual rotors and inertial measurements at all time. Besides introducing the dataset, this paper sheds light on the specific properties, challenges and opportunities brought by the emerging task of UAV-embedded sound source localization. Several baseline methods are evaluated and compared on the dataset, with real-time applicability in mind. Very promising results are obtained for the localization of a broad-band source in loud noise conditions, while speech localization remains a challenge under extreme noise levels

Sound Representation and Classification Benchmark for Domestic Robots

International audienceWe address the problem of sound representation and classification and present results of a comparative study in the context of a domestic robotic scenario. A dataset of sounds was recorded in realistic conditions (background noise, presence of several sound sources, reverberations, etc.) using the humanoid robot NAO. An extended benchmark is carried out to test a variety of representations combined with several classifiers. We provide results obtained with the annotated dataset and we assess the methods quantitatively on the basis of their classification scores, computation times and memory requirements. The annotated dataset is publicly available at https://team.inria.fr/perception/nard/

Audio-Motor Integration for Robot Audition

International audienceIn the context of robotics, audio signal processing in the wild amounts to dealing with sounds recorded by a system that moves and whose actuators produce noise. This creates additional challenges in sound source localization, signal enhancement and recognition. But the speci-ficity of such platforms also brings interesting opportunities: can information about the robot actuators' states be meaningfully integrated in the audio processing pipeline to improve performance and efficiency? While robot audition grew to become an established field, methods that explicitly use motor-state information as a complementary modality to audio are scarcer. This chapter proposes a unified view of this endeavour, referred to as audio-motor integration. A literature review and two learning-based methods for audio-motor integration in robot audition are presented, with application to single-microphone sound source localization and ego-noise reduction on real data

Developing a Noise-Robust Beat Learning Algorithm for Music-Information Retrieval

The field of Music-Information Retrieval (Music-IR) involves the development of algorithms that can analyze musical audio and extract various high-level musical features. Many such algorithms have been developed, and systems now exist that can reliably identify features such as beat locations, tempo, and rhythm from musical sources. These features in turn are used to assist in a variety of music-related tasks ranging from automatically creating playlists that match specified criteria to synchronizing various elements, such as computer graphics, with a performance. These Music-IR systems thus help humans to enjoy and interact with music. While current systems for identifying beats in music are have found widespread utility, most of them have been developed on music that is relatively free of acoustic noise. Much of the music that humans listen to, though, is performed in noisy environments. People often enjoy music in crowded clubs and noisy rooms, but this music is much more challenging for Music-IR systems to analyze, and current beat trackers generally perform poorly on musical audio heard in such conditions. If our algorithms could accurately process this music, though, it would enable this music too to be used in applications such as automatic song selection, which are currently limited to music taken directly from professionally-produced digital files that have little acoustic noise. Noise-robust beat learning algorithms would also allow for additional types of performance augmentation which create noise and thus cannot be used with current algorithms. Such a system, for instance, could aid robots in performing synchronously with music, whereas current systems are generally unable to accurately process audio heard in conjunction with noisy robot motors. This work aims to present a new approach for learning beats and identifying both their temporal locations and their spectral characteristics for music recorded in the presence of noise. First, datasets of musical audio recorded in environments with multiple types of noise were collected and annotated. Noise sources used for these datasets included HVAC sounds from a room, chatter from a crowded bar, and fans and motor noises from a moving robot. Second, an algorithm for learning and locating musical beats was developed which incorporates signal processing and machine learning techniques such as Harmonic-Percussive Source Separation and Probabilistic Latent Component Analysis. A representation of the musical signal called the stacked spectrogram was also utilized in order to better represent the time-varying nature of the beats. Unlike many current systems, which assume that the beat locations will be correlated with some hand-crafted features, this system learns the beats directly from the acoustic signal. Finally, the algorithm was tested against several state-of-the-art beat trackers on the audio datasets. The resultant system was found to significantly outperform the state-of-the-art when evaluated on audio played in realistically noisy conditions.Ph.D., Electrical Engineering -- Drexel University, 201