Anthony Griffin

Real-Time Multiple Sound Source Localization and Counting Using a Circular Microphone Array

In this work, a multiple sound source localization and counting method is presented, that imposes relaxed sparsity constraints on the source signals. A uniform circular microphone array is used to overcome the ambiguities of linear arrays, however the underlying concepts (sparse component analysis and matching pursuit-based operation on the histogram of estimates) are applicable to any microphone array topology. Our method is based on detecting time-frequency (TF) zones where one source is dominant over the others. Using appropriately selected TF components in these “single-source” zones, the proposed method jointly estimates the number of active sources and their corresponding directions of arrival (DOAs) by applying a matching pursuit-based approach to the histogram of DOA estimates. The method is shown to have excellent performance for DOA estimation and source counting, and to be highly suitable for real-time applications due to its low complexity. Through simulations (in various signal-to-noise ratio conditions and reverberant environments) and real environment experiments, we indicate that our method outperforms other state-of-the-art DOA and source counting methods in terms of accuracy, while being significantly more efficient in terms of computational complexity.

Localizing multiple audio sources in a wireless acoustic sensor network

In this work, we propose a grid-based method to estimate the location of multiple sources in a wireless acoustic sensor network, where each sensor node contains a microphone array and only transmits direction-of-arrival (DOA) estimates in each time interval, reducing the transmissions to the central processing node. We present new work on modeling the DOA estimation error in such a scenario. Through extensive, realistic simulations, we show that our method outperforms other state-of-the-art methods, in both accuracy and complexity. We also present localization results of real recordings in an outdoor cell of a sensor network. HighlightsWe examine localization in a WASN where each node transmits DOA estimates.We perform DOA estimation error modeling and examine the merging of nearby sources.We present a real-time low-complexity method for localization of multiple sources.Results indicate the advantages of our method in accuracy/computational complexity.We present localization results of real recordings in an outdoor cell of a sensor network.

Single-Channel and Multi-Channel Sinusoidal Audio Coding Using Compressed Sensing

Compressed sensing (CS) samples signals at a much lower rate than the Nyquist rate if they are sparse in some basis. In this paper, the CS methodology is applied to sinusoidally modeled audio signals. As this model is sparse by definition in the frequency domain (being equal to the sum of a small number of sinusoids), we investigate whether CS can be used to encode audio signals at low bitrates. In contrast to encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do, we propose encoding few randomly selected samples of the time-domain description of the sinusoidal component (per signal segment). The potential of applying compressed sensing both to single-channel and multi-channel audio coding is examined. The listening test results are encouraging, indicating that the proposed approach can achieve comparable performance to that of state-of-the-art methods. Given that CS can lead to novel coding systems where the sampling and compression operations are combined into one low-complexity step, the proposed methodology can be considered as an important step towards applying the CS framework to audio coding applications.

Real-time multiple sound source localization using a circular microphone array based on single-source confidence measures

We propose a novel real-time adaptative localization approach for multiple sources using a circular array, in order to suppress the localization ambiguities faced with linear arrays, and assuming a weak sound source sparsity which is derived from blind source separation methods. Our proposed method performs very well both in simulations and in real conditions at 50% real-time.

Compressed sensing for OFDM UWB systems

This paper considers compressed sensing (CS) techniques for signal reconstruction and channel estimation in OFDM-based high-rate ultra wideband (UWB) communication systems. We employ a parallel CS structure that exploits frequency domain sparsity. We also consider multipath UWB channels in both the line-of-sight and non line-of-sight environments. UWB signal detection and channel estimation from sub-Nyquist analog projections is carried out using an optimized orthogonal matching pursuit algorithm and the smoothed ℓ0 algorithm. Simulation results demonstrate significant gains in the form of reliable signal recovery and channel estimation as well as dramatically sub-Nyquist sampling rates for the analog-to-digital converters while maintaining high data rates.

Localizing multiple audio sources from DOA estimates in a wireless acoustic sensor network

In this work we propose a method to estimate the position of multiple sources in a wireless acoustic sensor network, where each sensor node only transmits direction-of-arrival (DOA) estimates each time interval, minimizing the transmissions to the processing node. Our method is based on the intersection of DOA estimates with outlier removal, and as such is very computationally efficient. We explore the performance of our method through extensive simulations and real measurements.

Source counting in real-time sound source localization using a circular microphone array

Recently, we proposed an approach inspired by Sparse Component Analysis for real-time localization of multiple sound sources using a circular microphone array. The method was based on identifying time-frequency zones where only one source is active, reducing the problem to single-source localization for these zones. A histogram of estimated Directions of Arrival (DOAs) was formed and then processed to obtain improved DOA estimates, assuming that the number of sources was known. In this paper, we extend our previous work by proposing three different methods for counting the number of sources by looking for prominent peaks in the derived histogram based on: (a) performing a peak search, (b) processing an LPC-smoothed version of the histogram, (c) employing a matching pursuit-based approach. The third approach is shown to perform very accurately in simulated reverberant conditions and additive noise, and its computational requirements are very small.

Directional coding of audio using a circular microphone array

We propose a real-time method for coding an acoustic environment based on estimating the Direction-of-Arrival (DOA) and reproducing it using an arbitrary loudspeaker configuration or headphones. We encode the sound field with the use of one audio signal and side-information. The audio signal can be further encoded with an MP3 encoder to reduce the bitrate. We investigate how such coding can affect the spatial impression and sound quality of spatial audio reproduction. Also, we propose a lossless efficient compression scheme for the side-information. Our method is compared with other recently proposed microphone array based methods for directional coding. Listening tests confirm the effectiveness of our method in achieving excellent reconstruction of the sound field while maintaining the sound quality at high levels.

Encoding the sinusoidal model of an audio signal using compressed sensing

In this paper, the compressed sensing (CS) methodology is applied to the harmonic part of sinusoidally-modeled audio signals. As this part of the model is sparse by definition in the frequency domain, we investigate how CS can be used to encode this signal at low bitrates, instead of encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do. We extend our previous work by considering an improved system model, by comparing our model to other schemes, and exploring the effect of incorrectly reconstructed frames. We show that encouraging results can be obtained by our approach, although inferior at this point compared to state-of-the-art. Good performance is obtained using 24 bits per sinusoid as indicated by our listening tests.

Improved face-to-face communication using noise reduction and speech intelligibility enhancement

Significant improvements in intelligibility of speech in noise can be obtained by modifying the speech signal in the time and/or frequency domains. However, most speech intelligibility enhancement algorithms are designed to use clean speech as an input, and their performance suffers once the input speech signal-to-noise ratio decreases, a common case in face-to-face communication environments such as restaurants or cafés. In this work we investigate whether a particularly successful speech intelligibility enhancement system-spectral shaping and dynamic range compression-and various front-end noise reduction methods might be suitable in such environments. Our evaluations suggest that such a complete system would provide an increase in speech intelligibility equivalent to a gain of 10 dB input signal-to-noise ratio in the more challenging face-to-face communication environments.