Efficient audio signal processing for embedded systems

We investigated two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound "richer" and "fuller," using a combination of bass extension and dynamic range compression. We also developed an audio energy reduction algorithm for loudspeaker power management by suppressing signal energy below the masking threshold. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. We also designed the circuits to implement the AdaBoost-based analog classifier.PhDCommittee Chair: Anderson, David; Committee Member: Hasler, Jennifer; Committee Member: Hunt, William; Committee Member: Lanterman, Aaron; Committee Member: Minch, Bradle

Optimizing wide-area sound reproduction using a single subwoofer with dynamic signal decorrelation

A central goal in small room sound reproduction is achieving consistent sound energy distribution across a wide listening area. This is especially difficult at low-frequencies where room-modes result in highly position-dependent listening experiences. While numerous techniques for multiple-degree-of-freedom systems exist and have proven to be highly effective, this work focuses on achieving position-independent low-frequency listening experiences with a single subwoofer. The negative effects due to room-modes and comb-filtering are mitigated by applying a time-varying decorrelation method known as dynamic diffuse signal processing. Results indicate that spatial variance in magnitude response can be significantly reduced, although there is a sharp trade-off between the algorithm’s effectiveness and the resulting perceptual coloration of the audio signal.N/

An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony

In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies the inverse minimum phase filter such that the magnitude response of the maximum phase zeros of the RIR is not fully compensated. Subjective listening tests reveal that the proposed schemes generally produce better quality output speech than a comparable inversion technique

An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony

In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies the inverse minimum phase filter such that the magnitude response of the maximum phase zeros of the RIR is not fully compensated. Subjective listening tests reveal that the proposed schemes generally produce better quality output speech than a comparable inversion technique

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc

Loudspeaker Modelling with Recurrent Neural Networks

Digital twins of loudspeakers are a useful assets for fine-tuning purposes during the design and the manufacturing phase. They can serve as an alternative to real-time measurement for objective evaluation of adjustments made by digital signal processing. Binaural loudspeaker models could introduce a more repeatable framework for subjective listening and provide flexibility for remote work due to the reduced need for actual physical devices. Neural Networks are a well-proven tool for system identification of different audio hardware devices. This thesis project will focus on creating a digital twin of a multimedia stereo loudspeaker system by using stereo audio waveform as the input and a binaural recording of the system's playback as the target waveform for Recurrent Neural Network (RNN) training. The RNN architecture is inspired by the current state-of-the-art method for single channel audio effects modelling, and is adapted for the stereo waveform use case. Firstly, the RNN model is tested with different synthesized target data that simulates the real recorded data. This approach allows us to estimate the properties which are the most challenging for the RNN to learn. Secondly, the experiments are run with a real recorded, time-aligned dataset, and the RNN's performance is objectively evaluated by the Error-To-Signal Ratio (ESR). In the current state-of-the-art method on single channel audio modelling, the initial hidden state of the RNN is computed by using no-gradient startup inference to accumulate the hidden state over the first few hundred samples of the training sequence. The thesis project proposes a new method called Discontinuous Sequence Training (DISCO). The method prepares the training dataset according to the RNNs architecture’s hyper-parameter sequence length and the system's impulse response length, such that it allows for correct initialization of the initial hidden state without additional pre-training inference. DISCO reaches the training and inference precision of hidden state initialization in the current state-of-the-art method for black-box modelling with RNNs only by modifying the dataset

High Frequency Reproduction in Binaural Ambisonic Rendering

Humans can localise sounds in all directions using three main auditory cues: the differences in time and level between signals arriving at the left and right eardrums (interaural time difference and interaural level difference, respectively), and the spectral characteristics of the signals due to reflections and diffractions off the body and ears. These auditory cues can be recorded for a position in space using the head-related transfer function (HRTF), and binaural synthesis at this position can then be achieved through convolution of a sound signal with the measured HRTF. However, reproducing soundfields with multiple sources, or at multiple locations, requires a highly dense set of HRTFs. Ambisonics is a spatial audio technology that decomposes a soundfield into a weighted set of directional functions, which can be utilised binaurally in order to spatialise audio at any direction using far fewer HRTFs. A limitation of low-order Ambisonic rendering is poor high frequency reproduction, which reduces the accuracy of the resulting binaural synthesis. This thesis presents novel HRTF pre-processing techniques, such that when using the augmented HRTFs in the binaural Ambisonic rendering stage, the high frequency reproduction is a closer approximation of direct HRTF rendering. These techniques include Ambisonic Diffuse-Field Equalisation, to improve spectral reproduction over all directions; Ambisonic Directional Bias Equalisation, to further improve spectral reproduction toward a specific direction; and Ambisonic Interaural Level Difference Optimisation, to improve lateralisation and interaural level difference reproduction. Evaluation of the presented techniques compares binaural Ambisonic rendering to direct HRTF rendering numerically, using perceptually motivated spectral difference calculations, auditory cue estimations and localisation prediction models, and perceptually, using listening tests assessing similarity and plausibility. Results conclude that the individual pre-processing techniques produce modest improvements to the high frequency reproduction of binaural Ambisonic rendering, and that using multiple pre-processing techniques can produce cumulative, and statistically significant, improvements

Audio for Virtual, Augmented and Mixed Realities: Proceedings of ICSA 2019 ; 5th International Conference on Spatial Audio ; September 26th to 28th, 2019, Ilmenau, Germany

The ICSA 2019 focuses on a multidisciplinary bringing together of developers, scientists, users, and content creators of and for spatial audio systems and services. A special focus is on audio for so-called virtual, augmented, and mixed realities. The fields of ICSA 2019 are: - Development and scientific investigation of technical systems and services for spatial audio recording, processing and reproduction / - Creation of content for reproduction via spatial audio systems and services / - Use and application of spatial audio systems and content presentation services / - Media impact of content and spatial audio systems and services from the point of view of media science. The ICSA 2019 is organized by VDT and TU Ilmenau with support of Fraunhofer Institute for Digital Media Technology IDMT