In Car Audio

This chapter presents implementations of advanced in Car Audio Applications. The system is composed by three main different applications regarding the In Car listening and communication experience. Starting from a high level description of the algorithms, several implementations on different levels of hardware abstraction are presented, along with empirical results on both the design process undergone and the performance results achieved

FPGA-based architectures for acoustic beamforming with microphone arrays : trends, challenges and research opportunities

Over the past decades, many systems composed of arrays of microphones have been developed to satisfy the quality demanded by acoustic applications. Such microphone arrays are sound acquisition systems composed of multiple microphones used to sample the sound field with spatial diversity. The relatively recent adoption of Field-Programmable Gate Arrays (FPGAs) to manage the audio data samples and to perform the signal processing operations such as filtering or beamforming has lead to customizable architectures able to satisfy the most demanding computational, power or performance acoustic applications. The presented work provides an overview of the current FPGA-based architectures and how FPGAs are exploited for different acoustic applications. Current trends on the use of this technology, pending challenges and open research opportunities on the use of FPGAs for acoustic applications using microphone arrays are presented and discussed

DESIGN AND EVALUATION OF HARMONIC SPEECH ENHANCEMENT AND BANDWIDTH EXTENSION

Improving the quality and intelligibility of speech signals continues to be an important topic in mobile communications and hearing aid applications. This thesis explored the possibilities of improving the quality of corrupted speech by cascading a log Minimum Mean Square Error (logMMSE) noise reduction system with a Harmonic Speech Enhancement (HSE) system. In HSE, an adaptive comb filter is deployed to harmonically filter the useful speech signal and suppress the noisy components to noise floor. A Bandwidth Extension (BWE) algorithm was applied to the enhanced speech for further improvements in speech quality. Performance of this algorithm combination was evaluated using objective speech quality metrics across a variety of noisy and reverberant environments. Results showed that the logMMSE and HSE combination enhanced the speech quality in any reverberant environment and in the presence of multi-talker babble. The objective improvements associated with the BWE were found to be minima

Objective Assessment of Machine Learning Algorithms for Speech Enhancement in Hearing Aids

Speech enhancement in assistive hearing devices has been an area of research for many decades. Noise reduction is particularly challenging because of the wide variety of noise sources and the non-stationarity of speech and noise. Digital signal processing (DSP) algorithms deployed in modern hearing aids for noise reduction rely on certain assumptions on the statistical properties of undesired signals. This could be disadvantageous in accurate estimation of different noise types, which subsequently leads to suboptimal noise reduction. In this research, a relatively unexplored technique based on deep learning, i.e. Recurrent Neural Network (RNN), is used to perform noise reduction and dereverberation for assisting hearing-impaired listeners. For noise reduction, the performance of the deep learning model was evaluated objectively and compared with that of open Master Hearing Aid (openMHA), a conventional signal processing based framework, and a Deep Neural Network (DNN) based model. It was found that the RNN model can suppress noise and improve speech understanding better than the conventional hearing aid noise reduction algorithm and the DNN model. The same RNN model was shown to reduce reverberation components with proper training. A real-time implementation of the deep learning model is also discussed

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications

Digital Microphone Array - Design, Implementation and Speech Recognition Experiments

The instrumented meeting room of the future will help meetings to be more efficient and productive. One of the basic components of the instrumented meeting room is the speech recording device, in most cases a microphone array. The two basic requirements for this microphone array are portability and cost-efficiency, neither of which are provided by current commercially available arrays. This will change in the near future thanks to the availability of new digital MEMS microphones. This dissertation reports on the first successful implementation of a digital MEMS microphone array. This digital MEMS microphone array was designed, implemented, tested and evaluated and successfully compared with an existing analogue microphone array using a state-of-the-art ASR system and adaptation algorithms. The newly built digital MEMS microphone array compares well with the analogue microphone array on the basis of the word error rate achieved in an automated speech recognition system and is highly portable and economical