| 1. |
- Jiang, Meng, et al.
(författare)
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Indoor Silent Object Localization using Ambient Acoustic Noise Fingerprinting
- 2020
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Ingår i: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781728144603
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Konferensbidrag (refereegranskat)abstract
- Indoor localization has been a popular research subject in recent years. Usually, object localization using sound involves devices on the objects, acquiring data from stationary sound sources, or by localizing the objects with external sensors when the object generates sounds. Indoor localization systems using microphones have traditionally also used systems with several microphones, setting the limitations on cost efficiency and required space for the systems. In this paper, the goal is to investigate whether it is possible for a stationary system to localize a silent object in a room, with only one microphone and ambient noise as information carrier. A subtraction method has been combined with a fingerprint technique, to define and distinguish the noise absorption characteristic of the silent object in the frequency domain for different object positions. The absorption characteristics of several positions of the object is taken as comparison references, serving as fingerprints of known positions for an object. With the experiment result, the tentative idea has been verified as feasible, and noise signal based lateral localization of silent objects can be achieved.
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| 2. |
- Pasha, Shahab, et al.
(författare)
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A Survey on Ad Hoc Signal Processing : Applications, Challenges and State-of-the-Art Techniques
- 2019
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Ingår i: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). - : IEEE. - 9781728153414
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Konferensbidrag (refereegranskat)abstract
- In an era of ubiquitous digital devices with built-in microphones and recording capability, distributed microphone arrays of a few digital recording devices are the emerging recording tool in hands-free speech communications and immersive meetings. Such so-called ad hoc microphone arrays can facilitate high-quality spontaneous recording experiences for a wide range of applications and scenarios, though critical challenges have limited their applications. These challenges include unknown and changeable positions of the recording devices and sound sources, resulting in varying time delays of arrival between microphones in the ad hoc array as well as varying recorded sound power levels. This paper reviews state-of-the-art techniques to overcome these issues and provides insight into possible ways to make existing methods more effective and flexible. The focus of this paper is on scenarios in which the microphones are arbitrarily located in an acoustic scene and do not communicate directly or through a fusion centre.
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| 3. |
- Pasha, Shahab, et al.
(författare)
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Distributed microphone arrays, emerging speech and audio signal processing platforms : A review
- 2020
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Ingår i: Advances in Science, Technology and Engineering Systems. - : ASTES Journal. - 2415-6698. ; 5:4, s. 331-343
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Tidskriftsartikel (refereegranskat)abstract
- Given ubiquitous digital devices with recording capability, distributed microphone arrays are emerging recording tools for hands-free communications and spontaneous tele-conferencings. However, the analysis of signals recorded with diverse sampling rates, time delays, and qualities by distributed microphone arrays is not straightforward and entails important considerations. The crucial challenges include the unknown/changeable geometry of distributed arrays, asynchronous recording, sampling rate mismatch, and gain inconsistency. Researchers have recently proposed solutions to these problems for applications such as source localization and dereverberation, though there is less literature on real-time practical issues. This article reviews recent research on distributed signal processing techniques and applications. New applications benefitting from the wide coverage of distributed microphones are reviewed and their limitations are discussed. This survey does not cover partially or fully connected wireless acoustic sensor networks.
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| 4. |
- Pasha, Shahab, et al.
(författare)
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Machine-learnt Beamforming for Large Aperture 3D Microphone Arrays, An Industrial Application
- 2021
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Ingår i: IEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021. - : IEEE. - 9781665432870
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Konferensbidrag (refereegranskat)abstract
- An eight-element 3D microphone array is designed for source separation and noise cancellation applications in noisy and reverberant environments with multiple sound sources. In the first phase the non-negative matrix factorization is applied to each channel of the array to isolate the target signal from the mixture. In the second phase a machine learning approach is applied for designing a beamformer by the means of deep learning techniques to learn and reconstruct the target signal coefficients. The matrix factorization and machine-learnt beamforming are shown effective tools for speech and music analysis in this contribution they are adapted to a novel context of non-stationary industrial signals. It is also shown that the proposed 3D array is a more effective tool for capturing the acoustic scene compared with the 2D rectangular sub-array (only the four microphones in the front panel) in terms of noise suppression and signal quality. A comparison made the proposed machine-learnt beamforming method and the baseline analytical method suggests superior performance of the machine-learnt approach.
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| 5. |
- Pasha, Shahab, et al.
(författare)
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Multi-Channel Compression and Coding of Reverberant Ad-Hoc Recordings Through Spatial Autoregressive Modelling
- 2019
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Ingår i: 2019 30th Irish Signals and Systems Conference (ISSC). - : IEEE. - 9781728128009
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Konferensbidrag (refereegranskat)abstract
- Autoregressive modelling techniques such as multi-channel linear prediction are widely used for applications such as coding, dereverberation and compression of the speech signals. State of the art multi-channel linear prediction methods do not take into account the locations of the microphones and assume single distance compact microphone arrays. In this paper a spatially modified multichannel autoregressive compression and coding method is proposed and successfully tested in order to adapt the standard multi-channel method to the virtual reality and immersive video conferencing applications where the microphones can be meters away from each other. The proposed method estimates the spatial distances between each microphone and the source to optimise the joint compression of the signals recorded within a wide area. The results suggest that the proposed method outperforms the standard multi-channel compression and coding when applied to the ad-hoc scenarios.
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| 6. |
- Pasha, Shahab, et al.
(författare)
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Multi-channel electronic stethoscope for enhanced cardiac auscultation using beamforming and equalisation techniques
- 2021
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Ingår i: 28th European Signal Processing Conference (EUSIPCO). - : IEEE. - 9789082797053 ; , s. 1289-1293
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Konferensbidrag (refereegranskat)abstract
- This paper reports on the implementation of a multi-channel electronic stethoscope designed to isolate the heart sound from the interfering sounds of the lungs and blood vessels. The multi-channel stethoscope comprises four piezo contact microphones arranged in rectangular and linear arrays. Beamforming and channel equalisation techniques are applied to the multi-channel recordings made in the aortic, pulmonary, tricuspid, and mitral valve areas. The proposed channel equaliser cancels out the distorting effect of the chest and rib cage on the heart sound frequency spectrum. It is shown that the applied beamforming methods effectively suppress the interfering lung noise and improve the signal to interference and noise ratio by 16 dB. The results confirm the superior performance of the implemented multi-channel stethoscope compared with commercially available single-channel electronic stethoscopes.
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| 7. |
- Pasha, Shahab, et al.
(författare)
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Two-stage artificial intelligence clinical decision support system for cardiovascular assessment using convolutional neural networks and decision trees
- 2020
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Ingår i: BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020. - : SciTePress. - 9789897583988 ; , s. 199-205
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Konferensbidrag (refereegranskat)abstract
- This paper describes an artificial-intelligence–assisted screening system implemented to support medical cardiovascular examinations performed by doctors. The proposed system is a two-stage supervised classifier comprising a convolutional neural network for heart murmur detection and a decision tree for classifying vital signs. The classifiers are trained to prioritize higher-risk individuals for more time-efficient assessment. A feature selection approach is applied to maximize classification accuracy by using only the most significant vital signs correlated with heart issues. The results suggest that the trained convolutional neural network can learn and detect heart sound anomalies from the time-domain and frequency-domain signals without using any user-guided mathematical or statistical features. It is also concluded that the proposed two-stage approach improves diagnostic reliability and efficiency. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
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