Audio pickup systems and methods are provided to enhance an audio signal by removing noise components related to an acoustic environment. The systems and methods receive a primary signal and a reference signal. The reference signal is adaptively filtered and subtracted from the primary signal to minimize an energy content of a resulting output signal.

A sound processing device includes a processor configured to generate a first frequency spectrum of a first sound signal corresponding to a first sound received at a first input device and a second frequency spectrum of a second sound signal corresponding to the first sound received at a second input device, calculate a transfer characteristic based on a first difference between an intensity of the first frequency spectrum and an intensity of the second frequency spectrum, generate a third frequency spectrum of a third sound signal transmitted from the first input device and a fourth frequency spectrum of a fourth sound signal transmitted from the second input device, specify a suppression level of an intensity of the fourth frequency spectrum based on a second difference between an intensity of the third frequency spectrum and an intensity of the fourth frequency spectrum.

Methods, systems and articles of manufacture for a wearable electronic device having an audio source identifier are disclosed. Example audio source identifiers disclosed herein include first and second audio sensors disposed at first and second locations, respectively, on a wearable electronic device. Such audio source identifiers also include a phase shift determiner to determine a phase shift between a first sample of first audio captured at the first audio sensor and a second sample of the first audio captured at the second audio sensor. The first audio includes first speech generated by a first speaker wearing the wearable electronic device. Example audio source identifiers further include a speaker identifier to determine, based on the phase shift determined by the phase shift determiner, whether second audio includes speech generated by a second speaker wearing the wearable electronic device.

Embodiments of the invention determine a speech estimate using a bone conduction sensor or accelerometer, without employing voice activity detection gating of speech estimation. Speech estimation is based either exclusively on the bone conduction signal, or is performed in combination with a microphone signal. The speech estimate is then used to condition an output signal of the microphone. There are multiple use cases for speech processing in audio devices.

The present invention relates to a method for audio signal processing in a vehicle. In order to allow simple and reliable echo cancellation for voice recognition during simultaneous reproduction of a multichannel audio source signal in a vehicle, a mono audio signal is generated on the basis of a multichannel audio source signal. The mono audio signal is limited to a frequency range between a prescribed lower frequency and a prescribed upper frequency, for example to a range from 100 Hz to 8 kHz. The limited mono audio signal is output via multiple loudspeakers in the vehicle. An influence of the limited mono audio signal that is output via the multiple loudspeakers on a voice audio signal received in the vehicle via a microphone is compensated for by means of the limited mono audio signal in an echo canceller.

An audio system includes an external microphone to receive a near-end audio content and a loudspeaker transducer and a corresponding enclosure defining an acoustic chamber. An internal pressure-gradient microphone is positioned in the acoustic chamber to detect a radiated output from the loudspeaker transducer. The audio system also includes a processor and a memory having instructions that, when executed by the processor, cause the audio system to receive a near-end signal from the external microphone and a reference signal from the internal microphone. The instructions, when executed, further cause the processor to cause the audio system to filter the reference signal from the near-end signal to define a clean near-end signal, and to emit the clean near-end signal. Related principles are described by way of reference to method and apparatus examples.

The present disclosure discloses a general speech enhancement method and apparatus using multi-source auxiliary information. The method includes following steps: S1: building a training data set; S2: using the training data set to learn network parameters of a model, and building a speech enhancement model; S3: building a sound source information database in a pre-collection or on-site collection mode; S4: acquiring an input of the speech enhancement model; and S5: taking a noisy original signal as a main input of the speech enhancement model, taking auxiliary sound signals of a target source group and auxiliary sound signals of an interference source group as side inputs of the speech enhancement model for speech enhancement, and obtaining an enhanced speech signal.

Embodiments of the present disclosure set forth a method of reducing noise in an audio signal. The method includes determining, based on sensor data acquired from a first set of sensors, a first position of a user in an environment. The method also includes acquiring, via the first set of sensors, one or more audio signals associated with sound in the environment and identifying one or more noise elements in the one or more audio signals. The method also includes generating a first directional audio signal based on the one or more noise elements. When the first directional audio signal is outputted by a first speaker, the first speaker produces a first acoustic field that attenuates the one or more noise elements at the first position.

A disclosed method includes monitoring an audio signal energy level while having a plurality of signal processing components deactivated and activating at least one signal processing component in response to a detected change in the audio signal energy level. The method may include activating and running a voice activity detector on the audio signal in response to the detected change where the voice activity detector is the at least one signal processing component. The method may further include activating and running the noise suppressor only if a noise estimator determines that noise suppression is required. The method may activate and runs a noise type classifier to determine the noise type based on information received from the noise estimator and may select a noise suppressor algorithm, from a group of available noise suppressor algorithms, where the selected noise suppressor algorithm is the most power consumption efficient.

A receiver circuit comprising a first-input-terminal configured to receive an analog-input-signal, which is representative of audio-data; and a second-input-terminal configured to receive a digital-input-signal, which is representative of the same audio-data as the analog-input-signal. The receiver circuit also includes a noise-estimator configured to determine a noise-signal that is representative of a difference between the analog-input-signal and the digital-input-signal; and a de-noiser that is configured to determine a de-noised-signal by applying a de-noising algorithm to the analog-input-signal based on the noise-signal.