NOISE REMOVAL USING MICROPHONE ARRAYS

ETSETB- Speech processing

Balthazar NEVEU

Problem

Problem simplification

[3D Room Geomety]

Speaker = 1signal + Position + Orientation

Video Projector = Noise + Position + Orientation

Microphone array: n-positions

Static speaker

Microphone responses synthesis

+

Nth ideal microphone room impulse response

Source= speakerSpeaker signal

Nth ideal microphone impulse room response

Source = video projectorNoise Signal

* Generic Microphone Impulse Response

geometry of the room, position of ideal microphone, source

Ideal Microphones

Advantage of sound synthesis Access to the exact signal

Enables to measure some of the speech signal features such as recognition rate

Control of signal to noise ratio

Number of microphones, positions…

Ideal microphone (flat impulse response)

Methods

Weiner Filtering with only one microphone

Beam forming in the direction of the speaker

Directional noise removal

Methods

1 microphone noise reductionWiener filtering

Methods Beam forming

Methods Directional cancellation

Compute the weights for beam forming under constraints of cancellation of the signal in the direction of the noise

Linear optimization

Quality Evaluation

2 methods Parameter: signal to noise ratio Objective measures

Increase in the rate of digit detection(HTK) Subjective measures

At different signal to noise ratio, for a group of k listeners for instance, vote if there’s an increase between a 1 microphone response and the processed signal

Quality Evaluation

SNR 10dB 20dB …

Wiener

Beam Forming

Directional noise removal

Digit recognition rate or voting results

Signal generation • Input signal

• Different SNR values • Digit list generation

• Output generation (N ideal microphones)

Noise reduction• Weiner filtering using 1 microphone• Beam Forming• Directional noise removal

Quality evaluation• Digit recognition (HTK)• Voting

Alternative method

ADAPTATIVE FILTERING d: desired signal

Use beam forming in the direction of the video projector to get the noise signal

x : for 1 microphone, use as input signal

x = h*d + speech signal => compute the filter

Alternative methodRLSLMS algorithmNLMS algorithm (adaptation of the

parameter of convergence) => can be fixed because the noise is already known at stationary in the long term

Filter can be precomputed in a room with no speaker for instance