INVESTIGATION OF SIGNAL THRESHOLDING EFFECTS ON THE ACCURACY OF SOUND SOURCE LOCALIZATION

In this article theoretical analysis of the signal thresholding effects on the accuracy of cross-correlation based sound source direction of arrival estimation was presented. The aim of the investigation was to determine the theoretical limits and challenges of the accuracy of the localization of a speaker within an acoustic enclosure by cross-correlation of two microphone signals and to offer means to increase the accuracy of sound source direction of arrival estimation via selection of audio frames based on the time lag estimation realiability measure. For the investigation, audio material from an openly accessible database was used. Presented are the methods for obtaining various features of the microphone signal frames, signal amplitude to minimum error amplitude calculation and experimentation with threshold-based audio frame selection.

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DATASET FOR EVALUATION OF THE PERFORMANCE OF THE METHODS OF SOUND SOURCE LOCALIZATION ALGORITHMS USING TETRAHEDRAL MICROPHONE ARRAYS

Mokslas - Lietuvos ateitis ◽

10.3846/mla.2020.11462 ◽

2020 ◽

Vol 12 (0) ◽

pp. 1-8

Author(s):

Saulius Sakavičius

Keyword(s):

Source Localization ◽

Sound Source ◽

Microphone Array ◽

Direction Of Arrival ◽

Microphone Arrays ◽

Sound Source Localization ◽

Direction Of Arrival Estimation ◽

Sound Sources ◽

Localization Algorithms ◽

Geometrical Information

For the development and evaluation of a sound source localization and separation methods, a concise audio dataset with complete geometrical information about the room, the positions of the sound sources, and the array of microphones is needed. Computer simulation of such audio and geometrical data often relies on simplifications and are sufficiently accurate only for a specific set of conditions. It is generally desired to evaluate algorithms on real-world data. For a three-dimensional sound source localization or direction of arrival estimation, a non-coplanar microphone array is needed.Simplest and most general type of non-coplanar array is a tetrahedral array. There is a lack of openly accessible realworld audio datasets obtained using such arrays. We present an audio dataset for the evaluation of sound source localization algorithms, which involve tetrahedral microphone arrays. The dataset is complete with the geometrical information of the room, the positions of the sound sources and the microphone array. Array audio data was captured for two tetrahedral microphone arrays with different distances between microphones and one or two active sound sources. The dataset is suitable for speech recognition and direction-of-arrival estimation, as the signals used for sound sources were speech signals.

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A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Sensors ◽

10.3390/s21062191 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2191

Author(s):

Huichao Yan ◽

Ting Chen ◽

Peng Wang ◽

Linmei Zhang ◽

Rong Cheng ◽

...

Keyword(s):

Compressed Sensing ◽

Spatial Clustering ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Cluster Center ◽

Direction Of Arrival Estimation ◽

Vector Hydrophone ◽

Regularization Parameters ◽

Selection Of

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

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