20 research outputs found
Deep Audio Zooming: Beamwidth-Controllable Neural Beamformer
Audio zooming, a signal processing technique, enables selective focusing and
enhancement of sound signals from a specified region, attenuating others. While
traditional beamforming and neural beamforming techniques, centered on creating
a directional array, necessitate the designation of a singular target
direction, they often overlook the concept of a field of view (FOV), that
defines an angular area. In this paper, we proposed a simple yet effective FOV
feature, amalgamating all directional attributes within the user-defined field.
In conjunction, we've introduced a counter FOV feature capturing directional
aspects outside the desired field. Such advancements ensure refined sound
capture, particularly emphasizing the FOV's boundaries, and guarantee the
enhanced capture of all desired sound sources inside the user-defined field.
The results from the experiment demonstrate the efficacy of the introduced
angular FOV feature and its seamless incorporation into a low-power subband
model suited for real-time applica?tions.Comment: 6 pages, 5 figure
MIMO-DoAnet: Multi-channel Input and Multiple Outputs DoA Network with Unknown Number of Sound Sources
Recent neural network based Direction of Arrival (DoA) estimation algorithms
have performed well on unknown number of sound sources scenarios. These
algorithms are usually achieved by mapping the multi-channel audio input to the
single output (i.e. overall spatial pseudo-spectrum (SPS) of all sources), that
is called MISO. However, such MISO algorithms strongly depend on empirical
threshold setting and the angle assumption that the angles between the sound
sources are greater than a fixed angle. To address these limitations, we
propose a novel multi-channel input and multiple outputs DoA network called
MIMO-DoAnet. Unlike the general MISO algorithms, MIMO-DoAnet predicts the SPS
coding of each sound source with the help of the informative spatial covariance
matrix. By doing so, the threshold task of detecting the number of sound
sources becomes an easier task of detecting whether there is a sound source in
each output, and the serious interaction between sound sources disappears
during inference stage. Experimental results show that MIMO-DoAnet achieves
relative 18.6% and absolute 13.3%, relative 34.4% and absolute 20.2% F1 score
improvement compared with the MISO baseline system in 3, 4 sources scenes. The
results also demonstrate MIMO-DoAnet alleviates the threshold setting problem
and solves the angle assumption problem effectively.Comment: Accepted by Interspeech 202