Digital processing of speech signals

Handbook of the International Phonetic Association: A Guide to the Use of the International Phonetic Alphabet

The title of this technical report says almost everything: this is indeed \"a short bibliography on AI and the arts\". It is presented in four sections: General Arguments, Proposals, and Approaches (31 references); Artificial Intelligence in Music (124 references); Artificial Intelligence in Literature and the Performing Arts (13 references), and Artificial Intelligence and Visual Art (57 references). About a quarter of these have short abstracts. Creating a bibliography can be a monumental task, and this bibliography should be viewed as a good and useful start, though it is by no means complete. For comparison, consider the 4,585-entry bibliography Computer Applications in Music by Deta Davis (A-REditions). No direct comparison is intended (or possible), but my point is that many more papers are likely to exist. As a rough check, I looked for several pre-1990 AI and Music articles and books (including my own, of course) in the bibliography. Out of five papers from well-known sources, only one was listed. On the other hand, I discovered a number of papers in this report that were unknown to me, so I am grateful to have a new source of references. In their introduction, the authors acknowledge the need for more references and even offer.a cup of coffee in reward for each new one. I will be sending a number of contributions, so the next time anyone is in Vienna, the coffee is on me. I hope the authors will continue to collect abstracts and publish an updated report in the future.

Auditory Scene Analysis: The Perceptual Organization of Sound by Albert Bregman (review)

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Advanced Digital Signal Processing And Noise Reduction

Online detection of anomalous execution can be valuable for robot manipulation, enabling robots to operate more safely, determine when a behavior is inappropriate, and otherwise exhibit more common sense. By using multiple complementary sensory modalities, robots could potentially detect a wider variety of anomalies, such as anomalous contact or a loud utterance by a human. However, task variability and the potential for false positives make online anomaly detection challenging, especially for long-duration manipulation behaviors. In this paper, we provide evidence for the value of multimodal execution monitoring and the use of a detection threshold that varies based on the progress of execution. Using a data-driven approach, we train an execution monitor that runs in parallel to a manipulation behavior. Like previous methods for anomaly detection, our method trains a hidden Markov model (HMM) using multimodal observations from non-anomalous executions. In contrast to prior work, our system also uses a detection threshold that changes based on the execution progress. We evaluated our approach with haptic, visual, auditory, and kinematic sensing during a variety of manipulation tasks performed by a PR2 robot. The tasks included pushing doors closed, operating switches, and assisting able-bodied participants with eating yogurt. In our evaluations, our anomaly detection method performed substantially better with multimodal monitoring than single modality monitoring. It also resulted in more desirable ROC curves when compared with other detection threshold methods from the literature, obtaining higher true positive rates for comparable false positive rates.

Multimodal execution monitoring for anomaly detection during robot manipulation

Speech intelligibility improvement in car noise environment by voice transformation

In this paper, we propose a novel approach aiming at improving the intelligibility of speech in the context of in-car applications. Speech produced in noisy environments is subject to the Lombard effect which gathers a number of voice transformation effects compared to the speech produced in calm environments. To improve intelligibility of in car speech (radio, message alerts, …), we propose to modify the original speech signal by incorporating one of the important Lombard effect, namely the shift of the lower formant center frequencies away from the competing noise regions. The proposed approach exploits traditional Linear Prediction analysis and overlap and add synthesis. We explore several modification strategies and the merit of each modification is evaluated using both objective and subjective tests. It is in particular shown that the improvement of speech intelligibility in car noise is significantly improved for a majority of listeners.

Formant shifting for speech intelligibility improvement in car noise environment

A novel method of single channel speaker segregation using the group delay cross correlation function is proposed in this paper. The group delay function, which is the negative derivative of the phase spectrum, yields robust spectral estimates. Hence the group delay spectral estimates are first computed over frequency sub-bands after passing the speech signal through a bank of filters. The filter bank spacing is based on a multi-pitch algorithm that computes the pitch estimates of the competing speakers. An affinity matrix is then computed from the group delay spectral estimates of each frequency sub-band. This affinity matrix represents the correlations of the different sub-bands in the mixed broadband speech signal. The grouping of correlated harmonics present in the mixed speech signal is then carried out by using a new iterative graph cut method. The signals are reconstructed from the respective harmonic groups which represent individual speakers in the mixed speech signal. Spectrographic masks are then applied on the reconstructed signals to refine their perceptual quality. The quality of separated speech is evaluated using several objective and subjective criteria. Experiments on multi-speaker automatic speech recognition are conducted using mixed speech data from the GRID corpus. A cell phone based multimedia information retrieval system (MIRS) for multi-source meeting environments are also developed.

Group Delay Based Methods for Speaker Segregation and its Application in Multimedia Information Retrieval

In this paper, a single channel Kalman filtering in the spectral domain is proposed for acoustic echo and noise cancellation (AENC). An augmented extended Kalman filter is used in this work which is suitable for processing general complex-valued nonlinear and nonstationary signals. The near-end speech and background noise are modelled to make the filter robust against double-talk and against noise. In this work, the approach for echo cancellation does not depend on double-talk detector (DTD) to model the echo path, when signals other than the echoed far-end signal are present. The performance evaluations are conducted over the NOIZEUS corpus. The experimental results demonstrate that the ideal Kalman filter (with near-end speech parameters) outperforms other enhancement methods of echo cancellation in terms of subjective and objective measures. The non-ideal Kalman filter (when near-end speech parameters are obtained by an EM algorithm) also shows promising results after the ideal Kalman filter.

Karan Nathwani

Papers

Speech intelligibility improvement in car noise environment by voice transformation

Formant shifting for speech intelligibility improvement in car noise environment

Group Delay Based Methods for Speaker Segregation and its Application in Multimedia Information Retrieval

Joint Acoustic Echo and Noise Cancellation Using Spectral Domain Kalman Filtering in Double-Talk Scenario

Robust acoustic echo cancellation using Kalman filter in double talk scenario