Matti Hämäläinen

Filter-and-sum beamformer with adjustable filter characteristics

We introduce a polynomial filter structure for filter-and-sum beamforming applied to a microphone array application. The structure is a multidimensional extension of the well known Farrow structure, which has mainly been used for fractional delay filtering and interpolation of I-D signals. The proposed method enables an easy, smooth and efficient control of the beamforming filter characteristics by adjusting only a single control variable, e.g., for dynamic beam steering. The optimization method for polynomial beamforming filter design is presented and illustrated with simulations of beamforming filter characteristics. The design example is given for a linear array of four omni-directional microphones and a polynomial FIR filter with 20-tap delay lines.

Closed-form self-localization of asynchronous microphone arrays

The utilization of distributed microphone arrays in many speech processing applications such as beamforming and speaker localization rely on the precise knowledge of microphone locations. Several self-localization approaches have been presented in the literature but still a simple, accurate, and robust method for asynchronous devices is lacking. This work presents an analytical solution for estimating the positions and rotations of asynchronous loudspeaker equipped microphone arrays or devices. The method is based on emitting and receiving calibration signals from each device, and extracting the time of arrival (TOA) values. Utilizing the knowledge of array geometry in the TOA estimation is proposed to improve accuracy of translation. Results with measurements using four devices on a table surface demonstrates a mean translation error of 11 mm with standard deviation of 6 mm and mean z-axis rotation error of 0.11 (rad) with a standard deviation of 0.14 (rad) in contrast to computer vision annotations with 200 rotations and translation estimates.

Passive Temporal Offset Estimation of Multichannel Recordings of an Ad-Hoc Microphone Array

In recent years ad-hoc microphone arrays have become ubiquitous, and the capture hardware and quality is increasingly more sophisticated. Ad-hoc arrays hold a vast potential for audio applications, but they are inherently asynchronous, i.e., temporal offset exists in each channel, and furthermore the device locations are generally unknown. Therefore, the data is not directly suitable for traditional microphone array applications such as source localization and beamforming. This work presents a least squares method for temporal offset estimation of a static ad-hoc microphone array. The method utilizes the captured audio content without the need to emit calibration signals, provided that during the recording a sufficient amount of sound sources surround the array. The Cramer-Rao lower bound of the estimator is given and the effect of limited number of surrounding sources on the solution accuracy is investigated. A practical implementation is then presented using non-linear filtering with automatic parameter adjustment. Simulations over a range of reverberation and noise levels demonstrate the algorithms robustness. Using smartphones an average RMS error of 3.5 samples (at 48 kHz) was reached when the algorithms assumptions were met.

A track before detect approach for sequential Bayesian tracking of multiple speech sources

This paper describes a novel multiple acoustic source tracking method based on track before detect paradigm. Multiple particle filters are used to represent the state of all sources. Sources are detected and removed using a likelihood ratio obtained from particle weights. The weights are obtained by evaluating the likelihood of microphone pair phase difference. Tracking performance from recorded data with rich sequences of speech is presented using multiple object tracking metrics. Results show that the proposed method can detect and track multiple temporally overlapping speech sources as well as switching talkers even in weak signal-to-noise ratios.

Acoustic Echo Cancellation for Dynamically Steered Microphone Array Systems

A new method for integrating dynamically steered beamforming filters and acoustic echo cancellation is presented. The proposed method enables beam steering independent AEC processing without parallel AEC filters for each microphone input (a.k.a. AEC-first configuration). Especially for larger microphone arrays the proposed method can provide significant computational savings with comparable performance to AEC-first configuration.

Self-localization of wireless acoustic sensors in meeting rooms

This paper presents a passive acoustic self-localization and synchronization system, which estimates the positions of wireless acoustic sensors utilizing the signals emitted by the persons present in the same room. The system is designed to utilize common off-the-shelf devices such as mobile phones. Once devices are self-localized and synchronized, the system could be utilized by traditional array processing methods. The proposed calibration system is evaluated with real recordings from meeting scenarios. The proposed system builds on earlier work with the added contribution of this work is i) increasing the accuracy of positioning, and ii) introduction data-driven data association. The results show that improvement over the existing methods in all tested recordings with 10 smartphones.

Adaptive beamforming methods for dynamically steered microphone array systems

This paper introduces two methods for the integration of a dynamically steered beamforming filter with acoustic echo cancellation (AEC) and adaptive generalized sidelobe canceler (GSC) based beamforming methods. We evaluate the performance of a beamforming system with moving and changing source positions. Individual contributions of adaptive beamforming and steering independent AEC processing methods are evaluated for high level echo cancellation in a typical office environment. The results show that the proposed adaptive beamforming method increases the overall AEC performance even if GSC adaptation would be disturbed by dynamic beam steering.

Optimization of multirate crossover filters

A digital crossover filter design procedure is developed based on multirate complementary filters. The crossover optimization procedure includes the determination of the optimal multirate filter structure and the overall optimization of the cascaded linear phase multirate FIR filter system. The filter structure can be optimized for several cost criteria e.g. for minimal run-time memory or computational complexity. The following crossover filter optimization is based on frequency sampling method in the weighted least-mean-squared sense. The weighting function combines the weighting for the auditory system and the audio reproduction system.A digital crossover filter design procedure is developed based on multirate complementary filters. The crossover optimization procedure includes the determination of the optimal multirate filter structure and the overall optimization of the cascaded linear phase multirate FIR filter system. The filter structure can be optimized for several cost criteria e.g. for minimal run-time memory or computational complexity. The following crossover filter optimization is based on frequency sampling method in the weighted least-mean-squared sense. The weighting function combines the weighting for the auditory system and the audio reproduction system.