Interactive environmental audio spatialization technology has become commonplace in personal computers and is migrating into portable entertainment platforms (including cell phones) and multiplayer game servers (virtual online worlds). While the primary current application of this technology is 3D game sound track rendering, it is ultimately necessary in the implementation of any personal or sh...

Speech recognition can be improved by using visual information in the form of lip movements of the speaker in addition to audio information. To date, state-of-the-art techniques for audio-visual speech recognition continue to use audio and visual data of the same database for training their models. In this paper, we present a new approach to make use of one modality of an external dataset in ad...

This article presents an approach to link the uploaders of videos based on the audio track of the videos. Using a subset of the MediaEval [10] Placing Task's Flickr video set, which is labeled with the uploader's name, we conducted an experiment with a similar setup as a typical NIST speaker identification evaluation run. Based on the assumption that the audio might be matched in various ways (...

Environment recognition from digital audio for forensics application is a growing area of interest. However, compared to other branches of audio forensics, it is a less researched one. Especially less attention has been given to detect environment from files where foreground speech is present, which is a forensics scenario. In this paper, we perform several experiments focusing on the problems ...

We consider the task of recognizing and learning the environments for mobile robot using audio information. Environments are mainly characterized by different types of specific sounds. Using audio enables the system to capture a semantically richer environment, as compared to using visual information alone. The goal of this paper is to investigate suitable features and the design feasibility of...

Sound effect libraries are commonly used by sound designers in a range of industries. Taxonomies exist for the classification of sounds into groups based on subjective similarity, sound source or common environmental context. However, these taxonomies are not standardised, and no taxonomy based purely on the sonic properties of audio exists. We present a method using feature selection, unsuperv...

An online audio classiication and segmentation system is presented in this research, where audio recordings are classiied and segmented into speech, music, several types of environmental sounds and silence based on audio content analysis. This is the rst step of our continuing work towards a general content-based audio classiication and retrieval system. The extracted audio features include tem...

The content-based classification and retrieval of real-world audio clips is one of the challenging tasks in multimedia information retrieval. Although the problem has been well studied in the last two decades, most of the current retrieval systems cannot provide flexible querying of audio clips due to the mixed-type form (e.g., speech over music and speech over environmental sound) of audio inf...

iTunes Signature Maker (iTSM) uses a feature-driven audio editing algorithm to rapidly generate a short sonic signature of an iTunes music library. iTSM stitches together small segments of songs, driving a concatenative algorithm with spectral features intrinsic to the audio files themselves and with environmental features which describe how those files have been used. This paper describes the ...

In this paper we present a method to search for environmental sounds in large unstructured databases of user-submitted audio, using a general sound events taxonomy from ecological acoustics. We discuss the use of Support Vector Machines to classify sound recordings according to the taxonomy and describe two use cases for the obtained classificationmodels: a content-based web search interface fo...