Koninklijke Philips Corporation N.v.

Google Inc.

Oticon A/s

Huawei Technologies Co., Limited

Richard Heusdens

Willem Bastiaan Kleijn

Jesper Bruun Jensen

Richard C Hendriks

Yue Lang

Ulrik Kjems

Steven Leonardus Josephus Dimphina Elisabeth Van De Par

Wenyu Jin

Thomas Sherson

Nikolay D Gaubitch

Renat Vafin

Meng Guo

Guoqiang Zhang

Nicolle Hanneke Van Schijndel

Valery Stephanovich Kot

Jamal Amini

Yuan Zeng

Cees H Taal

A sound processing node is provided for an arrangement of sound processing nodes configured to receive a plurality of sound signals. The sound processing node comprises a processor configured to generate an output signal based on the plurality of sound signals weighted by a plurality of beamforming weights. The processor is configured to adaptively determine the plurality of beamforming weights on the basis of an adaptive linearly constrained minimum variance beamformer using a transformed version of a least mean squares formulation of a constrained gradient descent approach. The transformed version of the least mean squares formulation of the constrained gradient descent approach is based on a transformation of the least mean squares formulation of the constrained gradient descent approach to the dual domain.

Sound processing node of an arrangement of sound processing nodes

The application relates to a hearing device adapted for being located at or in a first ear of a user, or to be fully or partially implanted in the head at a first ear of a user, the hearing device comprising

Binaural beamformer filtering unit, a hearing system and a hearing device

A sound processing node for an arrangement of sound processing nodes is disclosed. The sound processing nodes being configured to receive a plurality of sound signals, wherein the sound processing node comprises a processor configured to determine a beamforming signal on the basis of the plurality of sound signals weighted by a plurality of weights, wherein the processor is configured to determine the plurality of weights using a transformed version of a linearly constrained minimum variance approach, the transformed version of the linearly constrained minimum variance approach being obtained by applying a convex relaxation to the linearly constrained minimum variance approach.

Methods and systems are provided for implementing a distributed algorithm for beam-forming (e.g., MVDR beam-forming) using a message-passing algorithm. The message-passing algorithm provides for computations to be performed in a distributed manner across a network, rather than in a centralized processing center or 'fusion center'. The message-passing algorithm may also function for any network topology, and may continue operations when various changes are made in the network (e.g., nodes appearing, nodes disappearing, etc.). Additionally, the message-passing algorithm may minimize the transmission power per iteration and, depending on the particular network, also may minimize the transmission power required for communication between network nodes.

Distributed beamforming based on message passing

Provided are methods and systems for calibrating a distributed sensor (e.g., microphone) array using time-of-flight (TOF) measurements for a plurality of spatially distributed acoustic events at the sensors. The calibration includes localization and gain equalization of the sensors. Accurate measurements of TOFs are obtained from spatially distributed acoustic events using a controlled signal emitted at known intervals by a moving acoustic source. A portable user device capable of playing out audio is used to produce a plurality of acoustic events (e.g., sound clicks) at known intervals of time and at different, but arbitrary locations based on the device being moved around in space by a user while producing the acoustic events. As such, the times of the acoustic event generation are known, and are spatially diverse. The calibration signals emitted by the acoustic source are designed to provide robustness to noise and reverberation.

Microphone autolocalization using moving acoustic source

Provided are methods and systems for finding the location of sensors (e.g., microphones) with unknown internal delays based on a set of events (e.g., acoustic events) with unknown event time. A localization algorithm may iteratively run to compute the acoustic event times, the observation delays, and the relative locations of the events and the sensors.

Self-localization for a set of microphones

The application relates to a method of providing a speech intelligibility predictor value for estimating an average listener's ability to understand of a target speech signal when said target speech signal is subject to a processing algorithm and/or is received in a noisy environment. The application further relates to a method of improving a listener's understanding of a target speech signal in a noisy environment and to corresponding device units. The object of the present application is to provide an alternative objective intelligibility measure, e.g. a measure that is suitable for use in a time-frequency environment. The invention may e.g. be used in audio processing systems, e.g. listening systems, e.g. hearing aid systems.

Speech intelligibility predictor and applications thereof

A method estimates noise power spectral density (PSD) in an input sound signal to generate an output for noise reduction of the input sound signal. The method includes storing frames of a digitized version of the input signal, each frame having a predefined number N2 of samples corresponding to a frame length in time of L=N/sampling frequency. It further includes performing a time to frequency transformation, deriving a periodogram comprising an energy content |Y|from the corresponding spectrum Y, applying a gain function G(k,m)=f(&#x3c3;(km),&#x3c3;), |Y(k,m)|), to estimate a noise energy level |&#x174;|in each frequency sample, where &#x3c3;is the speech PSD and &#x3c3;the noise PSD. It further includes dividing spectra into a number of sub-bands, and providing a first estimate |{circumflex over (N)}|of the noise PSD level in a sub-band and a second, improved estimate |{circumflex over (N)}|of the noise PSD level in a subband by applying a bias compensation factor B to the first estimate.

Noise spectrum tracking in noisy acoustical signals

An audio encoder in which two or more preferably different encoders cooperate to generate a joint encoded audio signal. Encoding parameters of the two or more encoders are optimized in response to a measure of distortion of the joint encoded audio signal in accordance with a predetermined criterion. The distortion. measure is preferably a perceptual distortion measure. In one encoder embodiment comprising a sinusoidal and a waveform encoder, a constant total bit rate for each audio frame is distributed between the two encoders so as to minimize perceptual distortion for both the first and the second encoder. Other embodiments consider a set of encoding parameters that is larger than only those that minimize the perceptual distortion of the first encoder. In some embodiments, perceptual distortion may be minimized by optimizing encoding via optimizing entire encoding templates, i.e. a complex set of encoding parameters, for the separate encoders. The separate encoders may either be cascaded or operate in parallel, or in a combination of these. Two or more audio segments are preferably taken into account in the optimizing procedure. A corresponding audio decoder comprises separate decoders corresponding to the separate encoders of the audio encoder that encoded the audio signal. Decoded signal parts from these decoders are then added to produce the final audio signal. The presented audio encoding is efficient and provides a high sound quality because the encoding scheme is flexible and adapts to specific demands for each audio excerpt.

Combined audio coding minimizing perceptual distortion

An encoding method is characterised by a step of encoding parameters of a given sinusoidal component in encoded frames either differentially relative to other components in the same frame or directly, i.e. without differential encoding. Whether the encoding is differential or direct is decided algorithmically. A first type of algorithm produces an optimal result using a method derived from graph theory. An alternative algorithm, which is less computing intensive, provides an approximate result by an iterative greedy search algorithm.

Frequency-differential encoding a sinusoidal model parameters

An apparatus and method of signal coding includes an analysis-by-synthesis algorithm for sinusoidal modeling. An input signal to be modeled is divided in time to produce a plurality of frames. Functions from a dictionary are selected to form an approximation of the section of the input signal contained in each frame, with the selection carried out based on a psychoacoustic norm. The function dictionary is made up of complex exponentials and these are selected iteratively to make up the section of the input signal contained in each frame. The psychoacoustic norm adapts after each iteration according to the changing masking threshold of the residual signal to be modeled in the next step.

Sinusoidal model based coding of audio signals

An improved representation of transients in audio signals comprises modifying transient locations in such a way that a transient can occur only at a beginning of a sinusoidal segment. The modification procedure comprises the steps:

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Richard Heusdens