Segmentation Of Audio Streams Research Articles

Audio texturedness indicator based on a direct and reverse short listening time analysis

In this paper, we present an objective evaluation of audio texturedness level motivated by a subjective study which emphasizes the relevance of a direct and reverse short listening time analysis. This study was based on 77 undergraduate engineering students, where the concept of audio texturedness relied on audio and image analogies. Audio texturedness evaluation of a large audio database using a discrete [1 - 5] texturedness scale was performed. As a result, an objective audio texturedness indicator is proposed based on the cumulative average signal informational content change in the direct and reverse directions. This bidirectional cumulative entropy tracking was done in analogy with a classical multidirectional homogeneity method for image texture discrimination. As expected, the proposed indicator has ranked the class of noise on the high end of the [1 - 5] scale, whereas highly motivational speech signals ranked low on the proposed scale due to the large variations in their average informational content in the direct and reverse directions. This audio texturedness indicator offers a continuum of audio classes, in contrast with the classical noise, speech, and music sound categorization. The relevance of the proposed objective indicator auditory parameters was explored for a maximum objective-subjective cross-correlation and illustrated in a preliminary audio stream segmentation application.

Neural-inspired segmentation of audio streams into phone-like units

Recent work has shown that the human preauditory and/or auditory cortex is likely to play a role in acoustic landmark processing, such as the recognition of syllable and phoneme boundaries. Neurons appear to track acoustic envelopes with neural activity corresponding well with acoustic landmarks. These structures have also been observed in non-human primates, suggesting that acoustic landmark processing could be present in non-human primates and have an evolutionary role. Should landmark processing occur in non-humans, it could provide new methods for approaching animal communication. In this work, we use a simplified computer model to examine envelopes of acoustic signals that were filtered through a series of octave spaced band-pass filters. Peaks are detected in the signal envelopes with closely spaced peaks suppressed for a period of time after the first detection. Preliminary work shows an ability to detect human phoneme boundaries with a recall of 45% and a precision of 72% on the TIMIT speech corpus. The TIMIT corpus contains phoneme-level transcriptions and successful matches were defined as detecting landmarks within 20 ms of the over quarter million TIMIT phoneme boundaries. We also show qualitative results of the system for segmenting non-human primate calls into phone-like units.

On the Information Geometry of Audio Streams With Applications to Similarity Computing

This paper proposes methods for information processing of audio streams using methods of information geometry. We lay the theoretical groundwork for a framework allowing the treatment of signal information as information entities, suitable for similarity and symbolic computing on audio signals. The theoretical basis of this paper is based on the information geometry of statistical structures representing audio spectrum features, and specifically through the bijection between the generic families of Bregman divergences and that of exponential distributions. The proposed framework, called Music Information Geometry, allows online segmentation of audio streams to metric balls where each ball represents a quasi-stationary continuous chunk of audio, and discusses methods to qualify and quantify information between entities for similarity computing. We define an information geometry that approximates a similarity metric space, redefine general notions in music information retrieval such as similarity between entities, and address methods for dealing with nonstationarity of audio signals. We demonstrate the framework on two sample applications for online audio structure discovery and audio matching.

A novel audio stream segmentation method for audio signal discrimination

In this letter, an online segmentation algorithm for audio signal discrimination is presented. By detecting abrupt changes in audio signal features and decimating them followed by strength thresholding, segment boundaries of the audio stream are obtained. The resulting segment boundaries provide efficiency and accuracy for the classification stage of audio signal discrimination system.

Efficient audio stream segmentation via the combined T/sup 2/ statistic and Bayesian information criterion

In many speech and audio applications, it is first necessary to partition and classify acoustic events prior to voice coding for communication or speech recognition for spoken document retrieval. In this paper, we propose an efficient approach for unsupervised audio stream segmentation and clustering via the Bayesian Information Criterion (BIC). The proposed method extends an earlier formulation by Chen and Gopalakrishnan. In our formulation, Hotelling's T/sup 2/-Statistic is used to pre-select candidate segmentation boundaries followed by BIC to perform the segmentation decision. The proposed algorithm also incorporates a variable-size increasing window scheme and a skip-frame test. Our experiments show that we can improve the final algorithm speed by a factor of 100 compared to that in Chen and Gopalakrishnan's while achieving a 6.7% reduction in the acoustic boundary miss rate at the expense of a 5.7% increase in false alarm rate using DARPA Hub4 1997 evaluation data. The approach is particularly successful for short segment turns of less than 2 s in duration. The results suggest that the proposed algorithm is sufficiently effective and efficient for audio stream segmentation applications.