Masking (illustration)

Gene

Reverberation

Robustness (evolution)

Telecommunications

Biochemistry

Computer science

Visual arts

Antenna (radio)

Acoustics

Filter (signal processing)

Monaural

Physics

Speaker recognition

Speech enhancement

Pattern recognition (psychology)

Time–frequency analysis

Artificial intelligence

Direction of arrival

Chemistry

Artificial neural network

Noise reduction

Speaker verification

Speech recognition

Computer vision

IEEE/ACM Transactions on Audio Speech and Language Processing

Deep learning-based time-frequency (T-F) masking has dramatically advanced monaural (single-channel) speech separation and enhancement. This study investigates its potential for direction of arrival (DOA) estimation in noisy and reverberant environments. We explore ways of combining T-F masking and conventional localization algorithms, such as generalized cross correlation with phase transform, as well as newly proposed algorithms based on steered-response SNR and steering vectors. The key idea is to utilize deep neural networks (DNNs) to identify speech dominant T-F units containing relatively clean phase for DOA estimation. Our DNN is trained using only monaural spectral information, and this makes the trained model directly applicable to arrays with various numbers of microphones arranged in diverse geometries. Although only monaural information is used for training, experimental results show strong robustness of the proposed approach in new environments with intense noise and room reverberation, outperforming traditional DOA estimation methods by large margins. Our study also suggests that the ideal ratio mask and its variants remain effective training targets for robust speaker localization.

Robust Speaker Localization Guided by Deep Learning-Based Time-Frequency Masking