Section: Application Domains
Description and structuration of audio streams
audio stream, audio detection, audio tracking, audio segmentation, audio descriptors, multimedia indexing, audiovisual integration, multimodality, information fusion, audio-visual descriptors,
Automatic tools to locate events in audio documents, structure them and browse through them as in textual documents are key issues in order to fully exploit most of the available audio documents (radio and television programmes and broadcasts, conference recordings, etc).
In this respect, defining and extracting meaningful characteristics from an audio stream aim at obtaining a structured representation of the document, thus facilitating content-based access or search by similarity.
Activities in METISS focus on sound class and event characterisation and tracking in audio contents for a wide variety of features and documents.
Detecting and tracking sound classes and events
Locating various sounds or broad classes of sounds, such as silence, music or specific events like ball hits or a jingle, in an audio document is a key issue as far as automatic annotation of sound tracks is concerned. Indeed, specific audio events are crucial landmarks in a broadcast. Thus, locating automatically such events enables to answer a query by focusing on the portion of interest in the document or to structure a document for further processing. Typical sound tracks come from radio or TV broadcasts, or even movies.
In the continuity of research carried out at IRISA for many years (especially by Benveniste, Basseville, André-Obrecht, Delyon, Seck, ...) the statistical test approach can be applied to abrupt changes detection and sound class tracking, the latter provided a statistical model for each class to be detected or tracked was previously estimated. For example, detecting speech segments in the signal can be carried out by comparing the segment likelihoods using a speech and a “non-speech” statistical model respectively. The statistical models commonly used typically represent the distribution of the power spectral density, possibly including some temporal constraints if the audio events to look for show a specific time structure, as is the case with jingles or words. As an alternative to statistical tests, hidden Markov models can be used to simultaneously segment and classify an audio stream. In this case, each state (or group of states) of the automaton represent one of the audio event to be detected. As for the statistical test approach, the hidden Markov model approach requires that models, typically Gaussian mixture models, are estimated for each type of event to be tracked.
In the area of automatic detection and tracking of audio events, there are three main bottlenecks. The first one is the detection of simultaneous events, typically speech with music in a speech/music/noise segmentation problem since it is nearly impossible to estimate a model for each event combination. The second one is the not so uncommon problem of detecting very short events for which only a small amount of training data is available. In this case, the traditional 100 Hz frame analysis of the waveform and Gaussian mixture modeling suffer serious limitations. Finally, typical approaches require a preliminary step of manual annotation of a training corpus in order to estimate some model parameters. There is therefore a need for efficient machine learning and statistical parameter estimation techniques to avoid this tedious and costly annotation step.
Describing multi-modal information
Applied to the sound track of a video, detecting and tracking audio events can provide useful information about the video structure. Such information is by definition only partial and can seldom be exploited by itself for multimedia document structuring or abstracting. To achieve these goals, partial information from the various media must be combined. By nature, pieces of information extracted from different media or modalities are heterogeneous (text, topic, symbolic audio events, shot change, dominant color, etc.) thus making their integration difficult. Only recently approaches to combine audio and visual information in a generic framework for video structuring have appeared, most of them using very basic audio information.
Combining multimedia information can be performed at various level of abstraction. Currently, most approaches in video structuring rely on the combination of structuring events detected independently in each media. A popular way to combine information is the hierarchical approach which consists in using the results of the event detection of one media to provide cues for event detection in the other media. Application specific heuristics for decision fusions are also widely employed. The Bayes detection theory provides a powerful theoretical framework for a more integrated processing of heterogeneous information, in particular because this framework is already extensively exploited to detect structuring events in each media. Hidden Markov models with multiple observation streams have been used in various studies on video analysis over the last three years.
The main research topics in this field are the definition of structuring events that should be detected on the one hand and the definition of statistical models to combine or to jointly model low-level heterogeneous information on the other hand. In particular, defining statistical models on low-level features is a promising idea as it avoids defining and detecting structuring elements independently for each media and enables an early integration of all the possible sources of information in the structuring process.
Recurrent audio pattern detection
A new emerging topic is that of motif discovery in large volumes of audio data, i.e. discovering similar units in an audio stream in an unsupervised fashion. These motifs can constitue some form of audio “miniatures” which characterize some potentially salient part of the audio content : key-word, jingle, etc...
This problem naturally requires the definition of a robuste metric between audio segments, but a key issue relies in an efficient search strategy able to handle the combinatorial complexity stemming from the competition between all possible motif hypotheses. An additional issue is that of being able to model adequately the collection of instances corresponding to a same motif (in this respect, the HMM framework certainly offers a reasonable paradigm).