https://www.cin.ufpe.br/~rps/Artigos/Face%20Detection%20-%20%20A%20Survey.pdf

Face Detection

Digital processing of speech signals

Recent developments in human motion analysis

Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data. Over the past decade, many supervised separation algorithms have been put forward. In particular, the recent introduction of deep learning to supervised speech separation has dramatically accelerated progress and boosted separation performance. This paper provides a comprehensive overview of the research on deep learning based supervised speech separation in the last several years. We first introduce the background of speech separation and the formulation of supervised separation. Then, we discuss three main components of supervised separation: learning machines, training targets, and acoustic features. Much of the overview is on separation algorithms where we review monaural methods, including speech enhancement (speech-nonspeech separation), speaker separation (multitalker separation), and speech dereverberation, as well as multimicrophone techniques. The important issue of generalization, unique to supervised learning, is discussed. This overview provides a historical perspective on how advances are made. In addition, we discuss a number of conceptual issues, including what constitutes the target source.

Supervised Speech Separation Based on Deep Learning: An Overview

A location system for commercial wireless telecommunication infrastructures (CMRRs). The system is an end-to-end solution having one or more location systems ( 42 ) for outputting requested locations of commercially available hand sets or mobile stations (not shown) based on, e.g. AMPS, NAMPS, CDMA or TDMA communication standards, for processing both local mobile station location requests and more global mobile station location requests via, e.g., Internet communication between a distributed network of location systems. The system uses a plurality of mobile station locating technologies including those based on: two-way TOA and TDOA; home base stations and distributed antenna provisioning. Further, the system can be modularly configured for use in location signaling environments ranging from urban, dense urban, suburban, rural, mountain to low traffic or isolated roadways. Accordingly, the system is useful for 911 emergency calls, tracking, routing, people and animal location including applications for confinement to and from certain areas.

Location of a mobile station using a plurality of commercial wireless infrastructures

I. Speech Enhancement.- 1 Constant Directivity Beamforming.- 2 Superdirective Microphone Arrays.- 3 Post-Filtering Techniques.- 4 Spatial Coherence Functions for Differential Microphones in Isotropic Noise Fields.- 5 Robust Adaptive Beamforming.- 6 GSVD-Based Optimal Filtering for Multi-Microphone Speech Enhancement.- 7 Explicit Speech Modeling for Microphone Array Speech Acquisition.- II. Source Localization.- 8 Robust Localization in Reverberant Rooms.- 9 Multi-Source Localization Strategies.- 10 Joint Audio-Video Signal Processing for Object Localization and Tracking.- III. Applications.- 11 Microphone-Array Hearing Aids.- 12 Small Microphone Arrays with Postfilters for Noise and Acoustic Echo Reduction.- 13 Acoustic Echo Cancellation for Beamforming Microphone Arrays.- 14 Optimal and Adaptive Microphone Arrays for Speech Input in Automobiles.- 15 Speech Recognition with Microphone Arrays.- 16 Blind Separation of Acoustic Signals.- IV. Open Problems and Future Directions.- 17 Future Directions for Microphone Arrays.- 18 Future Directions in Microphone Array Processing.

Microphone Arrays Signal Processing Techniques and Applications

Talker localization with microphone arrays has received significant attention lately as a means for the automated tracking of individuals in an enclosure and as a necessary component of any general purpose speech capture system. Several algorithmic approaches are available for speech source localization with multi-channel data. This chapter summarizes the current field and comments on the general merits and shortcomings of each genre. A new localization method is then presented in detail. By utilizing key features of existing methods, this new algorithm is shown to be significantly more robust to acoustical conditions, particularly reverberation effects, than the traditional localization techniques in use today.

Robust Localization in Reverberant Rooms

Conventional time-delay estimators exhibit dramatic performance degradations in the presence of multipath signals. This limits their application in reverberant enclosures, particularly when the signal of interest is speech and it may not possible to estimate and compensate for channel effects prior to time-delay estimation. This paper details an alternative approach which reformulates the problem as a linear regression of phase data and then estimates the time-delay through minimization of a robust statistical error measure. The technique is shown to be less susceptible to room reverberation effects. Simulations are performed across a range of source placements and room conditions to illustrate the utility of the proposed time-delay estimation method relative to conventional methods.

A robust method for speech signal time-delay estimation in reverberant rooms

A practical methodology for speech source localization with microphone arrays

The linear intersection (LI) estimator, which is a closed-form method for the localization of source positions given sensor array time-delay estimate information, is presented. The LI estimator is shown to be robust and accurate, to closely model the search-based ML estimator, and to outperform a benchmark algorithm. The computational complexity of the LI estimator is suitable for use in real-time microphone-array applications where search-based location algorithms may be infeasible.

M.S. Brandstein

Papers

Microphone Arrays Signal Processing Techniques and Applications

Robust Localization in Reverberant Rooms

A robust method for speech signal time-delay estimation in reverberant rooms

A practical methodology for speech source localization with microphone arrays

A closed-form location estimator for use with room environment microphone arrays