A GPU-accelerated real-time implementation of TRINICON-BSS for multiple separation units - Anderson, Craig; Meier, Stefan; Kellermann, Walter; Teal, Paul D.; Poletti, Mark A. (Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2014)
In this demonstration, a GPU-accelerated implementation of the second-order statistics version of the TRINICON (Triple-N independent component analysis for convolutive mixtures) blind source separation (BSS) algorithm is presented. The single-precision arithmetic separation performance is compared with a double-precision CPU (Central Processing Unit) reference implementation to establish the effect of parallel algorithms and the reduction of floating-point precision. Computational performance results are presented for a single 2-channel separation unit and multiple simultaneous 2-channel separation units to study the potential future use in a sensor array network with multiple 2-channel separation unit
Keywords: Blind source separation; parallel algorithms; adaptive filtering

Analysis of the performance and limitations of ICA-based relative impulse response identification - Meier, Stefan; Kellermann, Walter (23rd European Signal Processing Conference (EUSIPCO), 2015)
Estimating impulse responses for a single source is a crucial problem for many applications in audio signal processing, such as source extraction. Since absolute impulse responses are hard to identify, relative impulse responses or, equivalently, relative transfer functions are identified instead. Independent Component Analysis (ICA) for convolutivemixtures offers the possibility to determine relative impulse responses implicitly by separating the target source from interfering sources. In this paper, fundamental limitations of relative transfer function (RTF) estimation are analyzed by calculating least-squares (LS)-optimal estimates in adverse scenarios, where the influence of scatterers and reverberation on the performance must be accounted for. Hereupon, ICA-based RTF estimation in the TRINICON framework is compared with the LS-optimal estimate
Keywords: Independent component analysis; impulse response estimation; relative transfer functions

Artificial neural network-based feature combination for spatial voice activity detection - Meier, Stefan; Kellermann, Walter (Interspeech, 2016)
For many applications in speech communications and speech-based human-machine interaction, a reliable Voice Activity Detection (VAD) is crucial. Conventional methods for VAD typically differentiate between a target speaker and background noise by exploiting characteristic properties of speech signals. If a target speaker should be distinguished from other speech sources, these conventional concepts are no longer applicable, and other methods, typically exploiting the spatial diversity of the individual sources, are required. Often, it is beneficial to combine several features in order to improve the overall decision. Optimum combinations of features, however, depend strongly on the scenario, especially on the position of the target source, the characteristics of noise and interference and the Signal-to-Interference Ratio (SIR). Moreover, choosing detection thresholds which are robust to changing scenarios is often a difficult problem. In this paper, these issues are addressed by introducing Artificial Neural Networks (ANNs) for spatial voice activity detection, which allow to combine several features with background information. The experimental results show that already small ANNs can significantly and robustly improve the detection rates, offering a valuable tool for VAD.
Keywords: voice activity detection; artificial neural network; generalized cross-correlation; receiver operating characteristic

A stereophonic acoustic front-end for distant-talking interfaces based on blind source separation - Reindl, Klaus; Maas, Roland; Schwarz, Andreas; Zheng, Yuanhang; Meier, Stefan; Sehr, Armin; Kellermann, Walter (Deutsche Jahrestagung für Akustik (DAGA), 2012)
In this contribution, an acoustic front-end for distanttalking interfaces that only requires two microphone signals is presented. It comprises a directional blind source separation(BSS)-based noise and interference estimation scheme and Wiener-type filters for noise and interference suppression. The proposed front-end and its integration into a speech recognition system is analyzed and evaluated in noisy living-room-like environments representing a generalization of the PASCAL CHiME challenge, implying that the potential of the method is verified for a wide range of distance

A stereophonic acoustic signal extraction scheme for noisy and reverberant environments - Reindl, Klaus; Zheng, Yuanhang; Schwarz, Andreas; Meier, Stefan; Maas, Roland; Sehr, Armin; Kellermann, Walter (Computer Speech and Language, 2012)
In this contribution, a novel two-channel acoustic front-end for robust automatic speech recognition in adverse acoustic environments with nonstationary interference and reverberation is proposed. From a MISO system perspective, a statistically optimum source signal extraction scheme based on the multichannel Wiener filter (MWF) is discussed for application in noisy and underdetermined scenarios. For free-field and diffuse noise conditions, this optimum scheme reduces to a Delay & Sum beamformer followed by a single-channel Wiener postfilter. Scenarios with multiple simultaneously interfering sources and background noise are usually modeled by a diffuse noise field. However, in reality, the free-field assumption is very weak because of the reverberant nature of acoustic environments. Therefore, we propose to estimate this simplified MWF solution in each frequency bin separately to cope with reverberation. We show that this approach can very efficiently be realized by the combination of a blocking matrix based on semi-blind source separation ('directional BSS'), which provides a continuously updated reference of all undesired noise and interference components separated from the desired source and its reflections, and a single-channel Wiener postfilter. Moreover, it is shown, how the obtained reference signal of all undesired components can efficiently be used to realize the Wiener postfilter, and at the same time, generalizes well-known postfilter realizations. The proposed front-end and its integration into an automatic speech recognition (ASR) system are analyzed and evaluated in noisy living-room-like environments according to the PASCAL CHiME challenge. A comparison to a simplified front-end based on a free-field assumption shows that the introduced system substantially improves the speech quality and the recognition performance under the considered adverse conditions. © 2012 Elsevier Ltd. All rights reserve
UnivIS-Import:2015-03-09:Pub.2012.tech.IE.LEN.astere_9
Keywords: Blind source extraction; PASCAL CHiME challenge; Robust automatic speech recognition; Speech enhancement

A Two-Channel Acoustic Front-End for Robust Automatic Speech Recognition in Noisy and Reverberant Environments - Maas, Roland; Schwarz, Andreas; Zheng, Yuanhang; Reindl, Klaus; Meier, Stefan; Sehr, Armin; Kellermann, Walter (International Workshop on Machine Listening in Multisource Environments (CHiME), 2011)
An acoustic front-end for robust automatic speech recognition in noisy and reverberant environments is pro-posed in this contribution. It comprises a blind source separation-based signal extraction scheme and only re-quires two microphone signals. The proposed front-end and its integration into the recognition system is analyzed and evaluated in noisy living room-like environments ac-cording to the PASCAL CHiME challenge. The results show that the introduced system significantly improves the recognition performance compared to the challenge baselin

Efficient target activity detection based on recurrent neural networks - Gerber, Daniel; Meier, Stefan; Kellermann, Walter (Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2017)
This paper addresses the problem of Target Activity Detection (TAD) for binaural listening devices. TAD denotes the problem of robustly detecting the activity of a target speaker in a harsh acoustic environment, which comprises interfering speakers and noise (‘cocktail party scenario’). In previous work, it has been shown that employing a Feed-forward Neural Network (FNN) for detecting the target speaker activity is a promising approach to combine the advantage of different TAD features (used as network inputs). In this contribution, we exploit a larger context window for TAD and compare the performance of FNNs and Recurrent Neural Networks (RNNs) with an explicit focus on small network topologies as desirable for embedded acoustic signal processing systems. More specifically, the investigations include a comparison between three different types of RNNs, namely plain RNNs, Long Short-Term Memories, and Gated Recurrent Units. The results indicate that all versions of RNNs outperform FNNs for the task of TA
Keywords: voice activity detection; target activity detection; recurrent neural networks; binaural listening devices

Impact of coding noise on the convergence of blind source separation - Meier, Stefan; Kellermann, Walter (11. ITG Conference on Speech Communication, 2014)
In networks consisting of distributed microphones, signals from several spatially distant microphones are exploited for multichannel signal processing. This requires transmission of at least one microphone signal to another network node. In this paper, the effect of a predictive source coding scheme on the TRINICON-based Blind Source Separation (BSS) algorithm is evaluated. First, the propagation of the quantization noise into the separated output signals is analyzed and verified experimentally. Moreover, the effect of coding on the convergence of the algorithm itself is evaluated. For the experimental part of the analysis, the application of BSS in a binaural hearing aid scenario is considered. BSS proves to be very robust against small coding word lengths and exhibits a minor sensitivity on noise shapin

Matching the acoustic model to front-end signal processing for ASR in noisy and reverberant environments - Maas, Roland; Schwarz, Andreas; Reindl, Klaus; Zheng, Yuanhang; Meier, Stefan; Sehr, Armin; Kellermann, Walter (Deutsche Jahrestagung für Akustik (DAGA), 2012)
Distant-talking automatic speech recognition (ASR) represents an extremely challenging task. The major reason is that unwanted additive interference and reverberation are picked up by the microphones besides the desired signal. A hands-free human-machine interface should therefore comprise a powerful acoustic preprocessing unit in line with a robust ASR back-end. However, since perfect speech enhancement cannot be achieved in practice, the output of the front-end will always contain some residual interference and some distortion of the desired signal. It is hence of decisive importance to carefully adjust the hidden Markov models (HMMs) of the ASR system to the front-end. In this contribution, we present a two-channel acoustic front-end based on blind source separation along with Wiener filtering. For the front-end integration into the ASR system, different types of multi-style as well as adaptive training and HMM adaptation are investigated.

Minimum mutual information-based linearly constrained broadband signal extraction - Reindl, Klaus; Meier, Stefan; Barfuß, Hendrik; Kellermann, Walter (IEEE Transactions on Audio Speech and Language Processing, 2014)
In this contribution, the problem of broadband acoustic signal extraction is treated as a specific source separation problem, where the desired signal components are to be separated from all remaining undesired components. For this, we exploit the generic TRIple-N Independent component analysis for CON-volutive mixtures (TRINICON) framework. The TRINICON optimization criterion is complemented with linear constraints leading to the Linearly Constrained Minimum Mutual Information (LCMMI) criterion for desired signal extraction. A general linearly constrained update rule for iterative filter optimization is derived, which can efficiently be realized in a novel Minimum Mutual Information (MMI)-Generalized Sidelobe Canceler (GSC). The general treatment of the signal extraction problem using an MMI criterion provides several advantages: Firstly, new insights into the signal extraction problem can be derived by establishing links to both the original GSC and the Multichannel Wiener Filter (MWF). Secondly, by exploiting fundamental properties characteristic for speech and audio signals, complicated and often unreliable Voice Activity Detection (VAD)-based control mechanisms become unnecessary. Thirdly, the overall realization requires only prior information of the desired source position. An evaluation of the MMI-GSC for the double-talk situation with two concurrently active speech sources under reverberant and noisy conditions demonstrates the effectiveness of this novel approac
UnivIS-Import:2016-02-10:Pub.2014.tech.IE.LEN.minimu
Keywords: Generalized sidelobe canceler (GSC); Interference cancellation; Linearly constrained minimum mutual information (LCMMI); Linearly constrained minimum variance (LCMV); Wiener filter

On the impact of signal preprocessing for robust distant speech recognition in adverse acoustic environments - Reindl, Klaus; Zheng, Yuanhang; Meier, Stefan; Schwarz, Andreas; Kellermann, Walter (IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2012)
In this contribution, a two-channel acoustic front-end for robust automatic speech recognition (ASR) in adverse acoustic environments is analyzed. The source signal extraction scheme combines a blocking matrix based on semi-blind source separation, which provides a continuously updated reference of all undesired components separated from the desired signal and its reflections, and a single-channel Wiener postfilter. The postfilter is directly derived from the obtained noise and interference reference signal and hence, generalizes well-known postfilter realizations. The proposed front-end and its integration into an ASR system are analyzed and evaluated with respect to keyword accuracy under reverberant conditions with unpredictable and nonstationary interferences, and for different target source distances. Evaluating a simplified front-end based on free-field assumptions, an ideal front-end, where knowledge about the true undesired components is assumed, and comparing the proposed scheme with the competitive approach of solely using multistyle training, demonstrates the importance of an adequate signal preprocessing for robust distant speech recognitio
Keywords: Blind source extraction; speech enhancement; robust distant speech recognition

Relative impulse response estimation during doubletalk with an artificial neural network-based step size control - Meier, Stefan; Kellermann, Walter (International Workshop on Acoustic Signal Enhancement (IWAENC), 2016)
The Normalized Least-Mean Squares (NLMS) algorithm is a widely used method for linear system identification (e.g., for Acoustic Echo Cancellation (AEC), where the acoustic path between loudspeaker and microphone needs to be estimated). As soon as interferers or background noise are active, step size control becomes a crucial task in order to ensure a fast but stable adaptation. Conventional step size control methods address the case of additive noise contaminating the system output, i.e., microphone signal. When the NLMS algorithm is used for Relative Impulse Response (RIR) estimation, however, both the input signal and the output signal of the unknown system are noisy (since both of them are microphone signals) and a different step size control is required. In this paper, the derivation of a new step size is presented. Since the resulting step size cannot be determined directly, an Artificial Neural Network (ANN)-based estimation of the step size is proposed and its effectiveness is demonstrate
Keywords: System Identification; Step Size Adaptation; Artificial Neural Networks

TRINICON-BSS system incorporating robust dual beamformers for noise reduction - Anderson, Craig; Meier, Stefan; Kellermann, Walter; Teal, Paul D.; Poletti, Mark A. (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015)
In this paper, a method of adaptive noise suppression combining spatially robust fixed beamforming and the TRINICON blind source separation algorithm is presented. A multichannel sensor array is first processed using complementary fixed beamformers into maximum and minimum SINR channels. The channels form the inputs to a single 2×2 second-order statistics TRINICON-BSS system which adaptively compensates for imperfections of the fixed beamformer design relative to the acoustic scenario. It is demonstrated that integrating the TRINICON-BSS algorithm leads to improved SINR performance over the initial imperfect beamformer design, and achieves a performance comparable to a perfect MVDR beamforme
Keywords: Robust beamforming; Adaptive filtering; Blind source separation