Eventness: Object Detection on Spectrograms for Temporal Localization of Audio Events

In this paper, we introduce the concept of Eventness for audio event detection, which can, in part, be thought of as an analogue to Objectness from computer vision. The key observation behind the eventness concept is that audio events reveal themselves as 2-dimensional time-frequency patterns with specific textures and geometric structures in spectrograms. These time-frequency patterns can then be viewed analogously to objects occurring in natural images (with the exception that scaling and rotation invariance properties do not apply). With this key observation in mind, we pose the problem of detecting monophonic or polyphonic audio events as an equivalent visual object(s) detection problem under partial occlusion and clutter in spectrograms. We adapt a state-of-the-art visual object detection model to evaluate the audio event detection task on publicly available datasets. The proposed network has comparable results with a state-of-the-art baseline and is more robust on minority events. Provided large-scale datasets, we hope that our proposed conceptual model of eventness will be beneficial to the audio signal processing community towards improving performance of audio event detection.Comment: 5 pages, 3 figures, accepted to ICASSP 201

A modified broadcast strategy for distributed signal estimation in a wireless sensor network with a tree topology

We envisage a wireless sensor network (WSN) where each node is tasked with estimating a set of node-specific desired signals that has been corrupted by additive noise. The nodes accomplish this estimation by means of the distributed adaptive node-specific estimation (DANSE) algorithm in a tree topology (T-DANSE). In this paper, we consider a network where there is at least one node with a large (virtually infinite) energy budget, which we select as the root node. We propose a modification to the signal flow of the T-DANSE algorithm where instead of each node having two-way signal communication, there is a single signal flow toward the root node of the tree topology which then broadcasts a single signal to all other nodes. We demonstrate that the modified algorithm is equivalent to the original T-DANSE algorithm in terms of the signal estimation performance, shifts a large part of the communication burden toward the high-power root node to reduce the energy consumption in the low-power nodes and reduces the input-output delay

Efficient calculation of sensor utility and sensor removal in wireless sensor networks for adaptive signal estimation and beamforming

Wireless sensor networks are often deployed over a large area of interest and therefore the quality of the sensor signals may vary significantly across the different sensors. In this case, it is useful to have a measure for the importance or the so-called "utility" of each sensor, e.g., for sensor subset selection, resource allocation or topology selection. In this paper, we consider the efficient calculation of sensor utility measures for four different signal estimation or beamforming algorithms in an adaptive context. We use the definition of sensor utility as the increase in cost (e.g., mean-squared error) when the sensor is removed from the estimation procedure. Since each possible sensor removal corresponds to a new estimation problem (involving less sensors), calculating the sensor utilities would require a continuous updating of different signal estimators (where is the number of sensors), increasing computational complexity and memory usage by a factor. However, we derive formulas to efficiently calculate all sensor utilities with hardly any increase in memory usage and computational complexity compared to the signal estimation algorithm already in place. When applied in adaptive signal estimation algorithms, this allows for on-line tracking of all the sensor utilities at almost no additional cost. Furthermore, we derive efficient formulas for sensor removal, i.e., for updating the signal estimator coefficients when a sensor is removed, e.g., due to a failure in the wireless link or when its utility is too low. We provide a complexity evaluation of the derived formulas, and demonstrate the significant reduction in computational complexity compared to straightforward implementations

On the use of time-domain widely linear filtering for binaural speech enhancement

Widely linear (WL) filtering has been shown to improve performance compared to linear filtering due to its ability to incorporate the non-circularity of the signal statistics. However there has been some inconsistency in its application, specifically when constructing complex signals from real signals, which has recently been considered in the context of speech enhancement in binaural or stereo systems. This letter shows that the corresponding WL filtered output contains exactly the same information as the linear filter output while increasing the computational complexity and memory requirements. © 1994-2012 IEEE.status: publishe

Efficient computation of microphone utility in a wireless acoustic sensor network with multi-channel wiener filter based noise reduction

A wireless acoustic sensor network is considered with spatially distributed microphones which observe a desired speech signal that has been corrupted by noise. In order to reduce the noise the signals are sent to a fusion center where they are processed with a centralized rank-1 multi-channel Wiener filter (R1-MWF). The goal of this work is to efficiently compute an assessment of the contribution of each individual microphone with respect to either signal-to-noise ratio (SNR), signal-to-distortion ratio (SDR) or the minimized cost function referred to as the utility. These performance measures are derived by exploiting unique properties of the R1-MWF which can be computed efficiently from values that are known from the current signal estimation process. The performance measures may be used in unison or individually to determine the contributions of each microphone and help facilitate in selecting only a subset of the available signals in order to meet the bandwidth and power constraints of the system. © 2012 IEEE.status: publishe

Topology-Independent Distributed Adaptive Node-Specific Signal Estimation in Wireless Sensor Networks

© 2015 IEEE. A topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm is presented where each node of a wireless sensor network (WSN) is tasked with estimating a node-specific desired signal. To reduce the amount of data exchange, each node applies a linear compression to its sensors signal observations, and only transmits the compressed observations to its neighbors. The TI-DANSE algorithm is shown to converge to the same optimal node-specific signal estimates as if each node were to transmit its raw (uncompressed) sensor signal observations to every other node in the WSN. The TI-DANSE algorithm is first introduced in a fully connected WSN and then shown, in fact, to have the same convergence properties in any topology. When implemented in other topologies, the nodes rely on an in-network summation of the transmitted compressed observations that can be accomplished by various means. We propose a method for this in-network summation via a data-driven signal flow that takes place on a tree, where the topology of the tree may change in each iteration. This makes the algorithm less sensitive to link failures and applicable to WSNs with dynamic topologies.status: publishe