33 accepted papers
In conventional multichannel audio signal enhancement, spatial and spectral filtering are often performed sequentially. In contrast, it has been shown that for neural spatial filtering a joint approach of spectro-spatial filtering is more beneficial. In this contribution, we investigate the spatial…
Spatial information can help improve source separation performance. Numerous spatially informed source extraction methods based on the independent vector analysis (IVA) have been developed, which can achieve reasonably good performance in non- or weakly reverberant environments. However, the perform…
Dereverberation, a process to mitigate or eliminate the reverberation effect, plays an important role in hands-free speech communication and human-machine interfaces. Tremendous efforts have been devoted to this problem and various methods have been developed over the last three decades. Those metho…
In this contribution, we present a novel online approach to multichannel speech enhancement. The proposed method estimates the enhanced signal through a filter-and-sum framework. More specifically, complex-valued masks are estimated by a deep complex-valued neural network, termed the complex-valued…
We present a novel end-to-end deep learning-based adaptation control algorithm for frequency-domain adaptive system identification. The proposed method exploits a deep neural network to map observed signal features to corresponding step-sizes which control the filter adaptation. The parameters of th…
Many spatial filtering algorithms used for voice capture in, e.g., teleconferencing applications, can benefit from or even rely on knowledge of Relative Transfer Functions (RTFs). Accordingly, many RTF estimators have been proposed which, however, suffer from performance degradation under acoustical…
Independent Vector Analysis (IVA) is an effective approach for Blind Source Separation (BSS) of convolutive mixtures of audio signals. As a practical realization of an IVA-based BSS algorithm, the so-called AuxIVA update rules based on the Majorize-Minimize (MM) principle have been proposed which al…
In this contribution, we introduce a novel approach to noise-robust acoustic echo cancellation employing a complex-valued Deep Neural Network (DNN) for postfiltering. In a first step, early linear echo components are removed using a double-talk robust adaptive filter. The residual signal is subseque…
Many algorithms for speech dereverberation and noise reduction rely on an estimate of the coherent-to-diffuse power ratio (CDR). Such systems typically operate in very diverse acoustic conditions, and CDR estimators relying on very weak model assumptions about the acoustic sound field of the desired…
In this paper, we consider the problem of acoustic source localization by acoustic sensor networks (ASNs) using a promising, learning-based technique that adapts to the acoustic environment. In particular, we look at the scenario when a node in the ASN is displaced from its position during training.…
To address the vital problem of selecting the most useful microphones in wireless acoustic sensor networks, this paper proposes a novel, general-purpose approach that accounts for both acoustic and network aspects and remains application-agnostic for broad applicability. The inter-channel correlatio…
We present a noise-robust adaptation control strategy for block-online supervised acoustic system identification by exploiting a noise dictionary. The proposed algorithm takes advantage of the pronounced spectral structure which characterizes many types of interfering noise signals. We model the noi…
While a common approach to address nonlinear distortions, emitted by multiple loudspeakers and observed by multiple microphones, is to use post-filtering techniques, this paper proposes a cooperative strategy to rather model and then cancel such distortions. In this approach, the overall problem of…
This contribution presents a novel approach for coherence-based signal enhancement. An estimator for the coherent-to-diffuse ratio (CDR) is devised, which exploits the concept of generalized magnitude coherence and thus, unlike common state-of-the-art schemes, can simultaneously take advantage of mo…
We present a Maximum A Posteriori (MAP) derivation of the Independent Vector Analysis (IVA) algorithm for blind source separation incorporating an additional spatial prior over the demixing matrices. In this way, the outer permutation ambiguity of IVA is avoided and the algorithm can be guided towar…
The suppression of ego-noise for (humanoid) robots is typically addressed by learning-based techniques. In this paper, we propose a novel approach which models significant parts of ego-noise spectrograms based on motor data and does not require a prior training step. Accordingly, the intrinsic harmo…
This paper investigates localization of an arbitrary number of simultaneously active speakers in an acoustic enclosure. We propose an algorithm capable of estimating the number of speakers, using reliability information to obtain robust estimation results in adverse acoustic scenarios and estimating…
In this paper, we propose a sequential training algorithm for feed-forward neural networks based on particle filtering. The proposed algorithm uses variational learning to tailor a proposal density by minimizing the variational energy. This density is then incorporated into the Gaussian particle fil…
The use of autonomous systems (ASs), such as humanoid robots, drones or self-driving vehicles, has expanded significantly in recent years. For such systems, acoustic scene analysis can provide useful information about the environment and supports the AS to react appropriately. However, compared to m…
We propose a method for estimating the distance between a sound source and a pair of recording microphones. The developed algorithm operates in the short-time Fourier transform domain and is based on estimates of the coherent-to-diffuse power ratio, which provides a measure for the amount of reverbe…
This paper considers an effective method for nonlinear acoustic echo cancellation (NL-AEC). More specifically, we model the nonlinear echo path by a latent state vector capturing the coefficients of a memoryless processor and a linear finite impulse response filter. To estimate the posterior probabi…
We propose a new concept for adapting CNN-based acoustic models using spatial diffuseness features as auxiliary information about the acoustic environment: the spatial diffuseness features are simultaneously employed as acoustic-model input features and to estimate environmental cues for context ada…
The performance of an ANC system largely depends on the availability of an accurate secondary path model. This is however a major challenge in multichannel ANC where the computational complexity increases significantly with the number of secondary sources and error sensors. This paper proposes wave-…
Most of multichannel sound source Direction Of Arrival (DOA) estimation algorithms suffer from spatial aliasing problems. The phase differences between a pair of microphones are wrapped beyond the spatial aliasing frequency. A common solution is to adjust the distance between the microphones to obta…
Uncertainty decoding combines a probabilistic feature description with the acoustic model of a speech recognition system. For DNN-HMM hybrid systems, this can be realized by averaging the DNN outputs produced by a finite set of feature samples (drawn from an estimated probability distribution). In t…
We address the problem of ego-noise reduction, i.e., suppressing the noise a robot causes by its own motions. Such noise degrades the recorded microphone signal massively such that the robot's auditory capabilities suffer. To suppress it, it is intuitive to use also motor data, since it provides add…
In reverberant environments, Listening Room Equalization (LRE) by pre-filtering of loudspeaker signals is highly desirable for premium sound reproduction systems with a high number of loudspeakers. In this contribution, the efficient concept of LRE by wave-domain adaptive filtering is extended by de…
The performance of sound reproduction systems for spatial audio is impaired by time-variant, reverberant listening environments. To tackle this issue, the Loudspeaker-Enclosure-Microphone System (LEMS) between the loudspeakers and reference microphones in the listening environment can be identified…
Many applications in audio communication require the identification of Loudspeaker-Enclosure-Microphone Systems (LEMS) with multiple inputs and outputs. The according computational complexity typically grows at least proportionally along the number of acoustic paths, which is the product of the numb…
Auditory systems of humanoid robots usually acquire the surrounding sound field by means of microphone arrays. These arrays can undergo motion related to the robot's activity. The conventional approach to dealing with this motion is to stop the robot during sound acquisition. This approach avoids ch…
We propose a novel sparse representation for heavily underdetermined multichannel sound mixtures, i.e., with much more sources than microphones. The proposed approach operates in the complex Fourier domain, thus preserving spatial characteristics carried by phase differences. We derive a generalizat…
We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the dire…
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 channel…