Acoustic Features for Environmental Sound Analysis

International audienceMost of the time it is nearly impossible to differentiate between particular type of sound events from a waveform only. Therefore, frequency domain and time-frequency domain representations have been used for years providing representations of the sound signals that are more inline with the human perception. However, these representations are usually too generic and often fail to describe specific content that is present in a sound recording. A lot of work have been devoted to design features that could allow extracting such specific information leading to a wide variety of hand-crafted features. During the past years, owing to the increasing availability of medium scale and large scale sound datasets, an alternative approach to feature extraction has become popular, the so-called feature learning. Finally, processing the amount of data that is at hand nowadays can quickly become overwhelming. It is therefore of paramount importance to be able to reduce the size of the dataset in the feature space. The general processing chain to convert an sound signal to a feature vector that can be efficiently exploited by a classifier and the relation to features used for speech and music processing are described is this chapter

Contribution to study and implementation of a bio-inspired perception system based on visual and auditory attention

The main goal of these researches is the design of one artificial perception system allowing to identify events or scenes in a complex environment. The work carried out during this thesis focused on the study and the conception of a bio-inspired perception system based on the both visual and auditory saliency. The main contributions of this thesis are auditory saliency with sound recognition and visual saliency with object recognition. The auditory saliency is computed by merging information from the both temporal and spectral signals with a saliency map of a spectrogram. The visual perception system is based on visual saliency and recognition of foreground object. In addition, the originality of the proposed approach is the possibility to do an evaluation of the coherence between visual and auditory observations using the obtained information from the features extracted from both visual and auditory patters. The experimental results have proven the interest of this method in the framework of scene identification in a complex environmentL'objectif principal de cette thèse porte sur la conception d'un système de perception artificiel permettant d'identifier des scènes ou évènements pertinents dans des environnements complexes. Les travaux réalisés ont permis d'étudier et de mettre en œuvre d'un système de perception bio-inspiré basé sur l'attention visuelle et auditive. Les principales contributions de cette thèse concernent la saillance auditive associée à une identification des sons et bruits environnementaux ainsi que la saillance visuelle associée à une reconnaissance d'objets pertinents. La saillance du signal sonore est calculée en fusionnant des informations extraites des représentations temporelles et spectrales du signal acoustique avec une carte de saillance visuelle du spectrogramme du signal concerné. Le système de perception visuelle est quant à lui composé de deux mécanismes distincts. Le premier se base sur des méthodes de saillance visuelle et le deuxième permet d'identifier l'objet en premier plan. D'autre part, l'originalité de notre approche est qu'elle permet d'évaluer la cohérence des observations en fusionnant les informations extraites des signaux auditifs et visuels perçus. Les résultats expérimentaux ont permis de confirmer l'intérêt des méthodes utilisées dans le cadre de l'identification de scènes pertinentes dans un environnement complex