Multiple Sound Source Localisation with Steered Response Power Density and Hierarchical Grid Refinement
March 04, 2018 ยท Declared Dead ยท ๐ IEEE/ACM Transactions on Audio Speech and Language Processing
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mert Burkay Coteli, Orhun Olgun, Huseyin Hacihabiboglu
arXiv ID
1803.01339
Category
cs.SD: Sound
Cross-listed
cs.MM,
eess.AS
Citations
35
Venue
IEEE/ACM Transactions on Audio Speech and Language Processing
Last Checked
6 months ago
Abstract
Estimation of the direction-of-arrival (DOA) of sound sources is an important step in sound field analysis. Rigid spherical microphone arrays allow the calculation of a compact spherical harmonic representation of the sound field. A basic method for analysing sound fields recorded using such arrays is steered response power (SRP) maps wherein the source DOA can be estimated as the steering direction that maximises the output power of a maximally-directive beam. This approach is computationally costly since it requires steering the beam in all possible directions. This paper presents an extension to SRP called steered response power density (SRPD) and an associated, signal-adaptive search method called hierarchical grid refinement (HiGRID) for reducing the number of steering directions needed for DOA estimation. The proposed method can localise coherent as well as incoherent sources while jointly providing the number of prominent sources in the scene. It is shown to be robust to reverberation and additive white noise. An evaluation of the proposed method using simulations and real recordings under highly reverberant conditions as well as a comparison with state- of-the-art methods are presented.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Sound
๐ฎ
๐ฎ
The Ethereal
R.I.P.
๐ป
Ghosted
Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
R.I.P.
๐ป
Ghosted
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
R.I.P.
๐ป
Ghosted
TasNet: time-domain audio separation network for real-time, single-channel speech separation
R.I.P.
๐ป
Ghosted
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
R.I.P.
๐ป
Ghosted
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted