Dorian Cazau

Understanding the intentional acoustic behavior of humpback whales: a production-based approach.

Following a production-based approach, this paper deals with the acoustic behavior of humpback whales. This approach investigates various physical factors, which are either internal (e.g., physiological mechanisms) or external (e.g., environmental constraints) to the respiratory tractus of the whale, for their implications in sound production. This paper aims to describe a functional scenario of this tractus for the generation of vocal sounds. To do so, a division of this tractus into three different configurations is proposed, based on the air recirculation process which determines air sources and laryngeal valves. Then, assuming a vocal function (in sound generation or modification) for several specific anatomical components, an acoustic characterization of each of these configurations is proposed to link different spectral features, namely, fundamental frequencies and formant structures, to specific vocal production mechanisms. A discussion around the question of whether the whale is able to fully exploit the acoustic potential of its respiratory tractus is eventually provided.

A study of vocal nonlinearities in humpback whale songs: from production mechanisms to acoustic analysis

Although mammalian vocalizations are predominantly harmonically structured, they can exhibit an acoustic complexity with nonlinear vocal sounds, including deterministic chaos and frequency jumps. Such sounds are normative events in mammalian vocalizations, and can be directly traceable to the nonlinear nature of vocal-fold dynamics underlying typical mammalian sound production. In this study, we give qualitative descriptions and quantitative analyses of nonlinearities in the song repertoire of humpback whales from the Ste Marie channel (Madagascar) to provide more insight into the potential communication functions and underlying production mechanisms of these features. A low-dimensional biomechanical modeling of the whale’s U-fold (vocal folds homolog) is used to relate specific vocal mechanisms to nonlinear vocal features. Recordings of living humpback whales were searched for occurrences of vocal nonlinearities (instabilities). Temporal distributions of nonlinearities were assessed within sound units, and between different songs. The anatomical production sources of vocal nonlinearities and the communication context of their occurrences in recordings are discussed. Our results show that vocal nonlinearities may be a communication strategy that conveys information about the whale’s body size and physical fitness, and thus may be an important component of humpback whale songs.

An investigation of prior knowledge in Automatic Music Transcription systems

Automatic transcription of music is a long-studied research field with many operational systems available commercially. In this paper, a generic transcription system able to host various prior knowledge parameters has been developed, followed by an in-depth investigation of their impact on music transcription. Explicit links between musical knowledge and algorithmic formalism have been made. Musical knowledge covers classes of timbre, musicology, and playing style of an instrument repertoire. An evaluation sound corpus gathering musical pieces played by human performers from three different instrument repertoires, namely, classical piano, steel-string acoustic guitar, and the marovany zither from Madagascar, has been developed. The different components of musical knowledge have been successively incorporated in a complete transcription system, consisting mainly of a Probabilistic Latent Component Analysis algorithm post-processed with a Hidden Markov Model, and their impact on transcription results have been comparatively evaluated.

Measuring the marine soundscape of the indian ocean with southern elephant seals used as acoustic gliders of opportunity

AbstractThe underwater ambient sound field contains quantifiable information about the physical and biological marine environment. The development of operational systems for monitoring in an autonomous way the underwater acoustic signal is necessary for many applications, such as meteorology and biodiversity protection. This paper develops a proof-of-concept study on performing marine soundscape analysis from acoustic passive recordings of free-ranging biologged southern elephant seals (SES). A multivariate multiple linear regression (MMLR) framework is used to predict the measured ambient noise, modeled as a multivariate acoustic response, from SES (depth, speed, and acceleration) and environmental (wind) variables. Results show that the acoustic contributions of SES variables affect mainly low-frequency sound pressure levels (SPLs), while frequency bands above 3 kHz are less corrupted by SES displacement and allow a good measure of the Indian Ocean soundscape. Also, preliminary results toward the develo...

Soundscape analysis in the Southern Ocean using elephant seals as acoustic glider of opportunity

The underwater ambient sound field contains quantifiable information about the physical and biological marine environment. Since 2011, we have been annually collecting underwater data over the migratory routes of bio-logged Southern Elephant Seal (SES). As done with classical underwater gliders, we extract from these data very high resolution (approximately 30 min/400 m) ocean ambient noise measurements. In this conference, we present an overall picture of the low-to-medium frequency (10–6000 Hz) ambient noise distribution and its variability in time and space at a regional scale within the Indian Ocean. We detail our methodology to extract robustly the measurements usually performed on ocean ambient noise, such as sound pressure level over different frequency bands and their statistical percentiles. Also, we present our first attempts of exploiting acoustic recordings from bio-logged SES to infer surface wind speed. Wind maps from the ASCAT satellite (IFREMER, France) were used to study correlation relations between surface wind speed and acoustic content (e.g., the ratio of sound pressure levels at 1 and 6 kHz). In complement, we test SVM and Neural Network methods to estimate the presence of different classes of winds (e.g., below and above 10 m/s) from underwater ocean noise.