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Publications (8 of 8) Show all publications
Nnonyelu, C. J., Jiang, M., Adamopoulou, M. & Lundgren, J. (2024). A Machine- Learning -based approach to Direction-of-arrival Sectorization using Spherical Microphone Array. In: 2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM): . Paper presented at 2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE
Open this publication in new window or tab >>A Machine- Learning -based approach to Direction-of-arrival Sectorization using Spherical Microphone Array
2024 (English)In: 2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM), IEEE, 2024Conference paper, Published paper (Refereed)
Abstract [en]

Direction of arrival estimation using the spherical microphone array usually requires a search in the whole 3-dimensional space, hence computationally demanding. This work presents a machine learning approach to sectorizing the 3-dimensional space, as an intermediate step for direction-of-arrival estimation using spherical microphone array. A new feature based on the outer product of spherical harmonic vectors was proposed for the classification. This spherical harmonic matrix nominally offers lower dimensionality compared to the commonly used covariance matrix of received data. The dimension of the input matrix was further reduced using the neighborhood component analysis. The extracted features were then used to train a support vector machine (SVM), 2-layer multilayer perceptron (MLP) and a convolutional neural network (CNN) for classification purposes. The results show that the models were able to classify the spherical sector with up to 90 % accuracy for all models and number of sectors under consideration. Also, the MLP and CNN trained with simulated samples were able to accurately classify samples from real data that were not included in training samples.

Place, publisher, year, edition, pages
IEEE, 2024
National Category
Signal Processing
Identifiers
urn:nbn:se:miun:diva-52259 (URN)10.1109/SAM60225.2024.10636592 (DOI)2-s2.0-85203352041 (Scopus ID)979-8-3503-4481-3 (ISBN)
Conference
2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM)
Projects
Acoustic sensor array design for AI monitoring system
Available from: 2024-08-28 Created: 2024-08-28 Last updated: 2024-09-17Bibliographically approved
Lundgren, J., Jiang, M., Laino, V., Gallo, V., Carratù, M. & Nnonyelu, C. J. (2024). Accuracy Impact of Increased Measurement Quality when using Pretrained Networks for Classification. In: Conference Record - IEEE Instrumentation and Measurement Technology Conference: . Paper presented at Conference Record - IEEE Instrumentation and Measurement Technology Conference. IEEE conference proceedings
Open this publication in new window or tab >>Accuracy Impact of Increased Measurement Quality when using Pretrained Networks for Classification
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2024 (English)In: Conference Record - IEEE Instrumentation and Measurement Technology Conference, IEEE conference proceedings, 2024Conference paper, Published paper (Refereed)
Abstract [en]

The field of Metrology has seen great use of Machine Learning and Deep Learning models, improving existing Metrology and enabling measurements and estimations that were previously not possible. In the challenging task of gathering training data in various areas of Metrology, a question arises; is it necessary to gather a completely new dataset every time a quality upgrade is done to a measurement system, method or model, or can the formerly trained model be used for new data with higher Signal-to-Noise Ratio (SNR)? This paper investigates how trained neural networks react to new data coming into the testing, with a higher SNR than the training data. In the experiments, Convolutional Neural Networks (CNN), in 1D and 2D, are used on heart sound data, as a test case. The initial results show that the classification accuracy for the new data, with a higher SNR, coming into the 1D CNN is almost as high as if the network had been trained on the higher SNR data. For a 2D CNN working with spectrograms instead of time series data, the change in accuracy is not nearly as high, as the 2D CNN model seems more robust to noise differences. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024
Keywords
Classification, Data Quality, Deep Learning, Machine Learning, Neural Networks, Pretrained, SNR
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:miun:diva-52050 (URN)10.1109/I2MTC60896.2024.10561016 (DOI)2-s2.0-85197767566 (Scopus ID)9798350380903 (ISBN)
Conference
Conference Record - IEEE Instrumentation and Measurement Technology Conference
Available from: 2024-08-07 Created: 2024-08-07 Last updated: 2024-08-07Bibliographically approved
Adamopoulou, M., Jiang, M., Nnonyelu, C. J., Carratù, M., Liguori, C. & Lundgren, J. (2024). Improving Cardiac Auscultation Signal Quality by using 4-Channel Stethoscope Array. In: Conference Record - IEEE Instrumentation and Measurement Technology Conference: . Paper presented at Conference Record - IEEE Instrumentation and Measurement Technology Conference. IEEE conference proceedings
Open this publication in new window or tab >>Improving Cardiac Auscultation Signal Quality by using 4-Channel Stethoscope Array
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2024 (English)In: Conference Record - IEEE Instrumentation and Measurement Technology Conference, IEEE conference proceedings, 2024Conference paper, Published paper (Refereed)
Abstract [en]

In cardiac auscultation, the ability to clearly hear any existing murmur sounds in heart sounds is crucial for proper diagnosis. This work aims to improve heart sound by the use of a stethoscope array and beamforming technique. The stethoscope array comprises four piezo elements for measurement, placed on the edges of a 40mm by 40mm rectangle. The directionality of the piezo elements reduces the effect of ambient noise in the measurement. The signal amelioration is achieved by isolating the systole and diastole sounds, and independently applying the delay-and-sum beamforming. This thereby makes any existing murmur sounds in the systole and/or diastole more audible and clearer to aid diagnosis. Finally, the designed stethoscope array and signal processing shows a gain of up to 33% for measured healthy heart samples, and up to 63% increase in murmur sound gain for measured sample with medically confirmed murmur. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024
Keywords
acoustic beam steering, auscultation, auscultation signal processing, stethoscope, stethoscope array
National Category
Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:miun:diva-52055 (URN)10.1109/I2MTC60896.2024.10560871 (DOI)2-s2.0-85197746698 (Scopus ID)9798350380903 (ISBN)
Conference
Conference Record - IEEE Instrumentation and Measurement Technology Conference
Available from: 2024-08-07 Created: 2024-08-07 Last updated: 2024-08-07Bibliographically approved
Jiang, M., Nnonyelu, C. J., Lundgren, J., Thungström, G. & Sjöström, M. (2023). A Coherent Wideband Acoustic Source Localization Using a Uniform Circular Array. Sensors, 23(11), Article ID 5061.
Open this publication in new window or tab >>A Coherent Wideband Acoustic Source Localization Using a Uniform Circular Array
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2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 11, article id 5061Article in journal (Refereed) Published
Abstract [en]

In modern applications such as robotics, autonomous vehicles, and speaker localization, the computational power for sound source localization applications can be limited when other functionalities get more complex. In such application fields, there is a need to maintain high localization accuracy for several sound sources while reducing computational complexity. The array manifold interpolation (AMI) method applied with the Multiple Signal Classification (MUSIC) algorithm enables sound source localization of multiple sources with high accuracy. However, the computational complexity has so far been relatively high. This paper presents a modified AMI for uniform circular array (UCA) that offers reduced computational complexity compared to the original AMI. The complexity reduction is based on the proposed UCA-specific focusing matrix which eliminates the calculation of the Bessel function. The simulation comparison is done with the existing methods of iMUSIC, the Weighted Squared Test of Orthogonality of Projected Subspaces (WS-TOPS), and the original AMI. The experiment result under different scenarios shows that the proposed algorithm outperforms the original AMI method in terms of estimation accuracy and up to a 30% reduction in computation time. An advantage offered by this proposed method is the ability to implement wideband array processing on low-end microprocessors.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
array manifold interpolation, direction of arrival estimation, wideband sources
National Category
Signal Processing
Identifiers
urn:nbn:se:miun:diva-48473 (URN)10.3390/s23115061 (DOI)001005309700001 ()37299788 (PubMedID)2-s2.0-85161608613 (Scopus ID)
Available from: 2023-06-12 Created: 2023-06-12 Last updated: 2023-06-30Bibliographically approved
Nnonyelu, C. J., Jiang, M. & Lundgren, J. (2023). Spherical-sector harmonics domain processing for wideband source localization using spherical-sector array of directional microphones. Paper presented at 184th Meeting of the Acoustical Society of America, Chicago, United of America. Journal of the Acoustical Society of America, 153(3_supplement), A54-A54
Open this publication in new window or tab >>Spherical-sector harmonics domain processing for wideband source localization using spherical-sector array of directional microphones
2023 (English)In: Journal of the Acoustical Society of America, ISSN 0001-4966, E-ISSN 1520-8524, Vol. 153, no 3_supplement, p. A54-A54Article in journal, Meeting abstract (Refereed) Published
Abstract [en]

The spherical microphone array can be uneconomical for applications where the sources arrive only from a known section of the sphere. For this reason, the spherical-sector harmonics was developed for processing spherical sector array. The orthonormal spherical sector harmonics (SSH) basis functions which accounts for the discontinuity arising from sectioning the sphere have been developed and shown to work for the array of omnidirectional microphones. In this work, the SSH basis functions are applied to far-field wideband sound source localization using spherical-sector array of first-order directional microphones (cardioid microphones). The array manifold interpolation method is used to produce the steered covariance matrix and the MUSIC algorithm applied for the direction of arrival estimation. The root-mean-square error performance of this spherical-sector array of the first-order cardioid microphones is compared against that of the omnidirectional microphones for different directions and signal-to-noise ratio.

Place, publisher, year, edition, pages
Acoustical Society of America (ASA), 2023
National Category
Signal Processing
Identifiers
urn:nbn:se:miun:diva-50017 (URN)10.1121/10.0018140 (DOI)
Conference
184th Meeting of the Acoustical Society of America, Chicago, United of America
Available from: 2023-12-02 Created: 2023-12-02 Last updated: 2024-02-19Bibliographically approved
Nnonyelu, C. J., Jiang, M. & Lundgren, J. (2022). A Lower Bound on the Estimation Variance of Direction-of-Arrival and Skew Angle of a Biaxial Velocity Sensor Suffering from Stochastic Loss of Perpendicularity. Sensors, 22(21), Article ID 8464.
Open this publication in new window or tab >>A Lower Bound on the Estimation Variance of Direction-of-Arrival and Skew Angle of a Biaxial Velocity Sensor Suffering from Stochastic Loss of Perpendicularity
2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 21, article id 8464Article in journal (Refereed) Published
Abstract [en]

The biaxial velocity sensor comprises two nominally perpendicular particle velocity sensors and a collocated pressure sensor. Due to real-world imperfections in manufacturing or setup errors, the two axes may suffer from perpendicularity losses. To analytically study how skewness affects its direction-finding performance, the hybrid Cramér-Rao bound (HCRB) of the directions-of-arrival for the polar angle, azimuth angle and the skew angle of a biaxial velocity sensor that suffers from stochastic loss of perpendicularity were derived in closed form. The skew angle was modeled as a zero-mean Gaussian random variable of a known variance, which was assumed to be very small, to capture the uncertainty in the orthogonality of the biaxial velocity sensor. The analysis shows that for the polar and azimuth angle, the loss of perpendicularity introduces the variation of the HCRB along the azimuth angle axis, which is independent of the skew angle, but on its variance. The dynamic range of this variation increases as the variance of the skew angle increases. For the estimation of the skew angle, the HCRB of the skew angle is bounded upwards by the variance of the skew angle and varies with the azimuth angle. The hybrid maximum likelihood- maximum a posterior (hybrid ML/MAP) estimator was used to verify the derived bounds. 

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-46404 (URN)10.3390/s22218464 (DOI)000884019700001 ()36366166 (PubMedID)2-s2.0-85141572074 (Scopus ID)
Available from: 2022-11-06 Created: 2022-11-06 Last updated: 2022-12-01Bibliographically approved
Jiang, M., Nnonyelu, C. J., Lundgren, J., Sjöström, M., Thungström, G. & Gao, S. (2022). Performance Comparison of Omni and Cardioid Directional Microphones for Indoor Angle of Arrival Sound Source Localization. In: Conference Record - IEEE Instrumentation and Measurement Technology Conference: . Paper presented at 2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022, 16 May 2022 through 19 May 2022. IEEE
Open this publication in new window or tab >>Performance Comparison of Omni and Cardioid Directional Microphones for Indoor Angle of Arrival Sound Source Localization
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2022 (English)In: Conference Record - IEEE Instrumentation and Measurement Technology Conference, IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

The sound source localization technology brings the possibility of mapping the sound source positions. In this paper, angle-of-arrival (AOA) has been chosen as the method for achieving sound source localization in an indoor enclosed environment. The dynamic environment and reverberations bring a challenge for AOA-based systems for such applications. By the acknowledgement of microphone directionality, the cardioid-directional microphone systems have been chosen for the localization performance comparison with omni-directional microphone systems, in order to investigate which microphone is superior in AOA indoor sound source localization. To reduce the hardware complexity, the number of microphones used during the experiment has been limited to 4. A localization improvement has been proposed with a weighting factor. The comparison has been done for both types of microphones with 3 different array manifolds under the same system setup. The comparison shows that the cardioid-directional microphone system has an overall higher accuracy. 

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
angle of arrival, array manifold, cardioid microphone, sound source localization
National Category
Computer Sciences Signal Processing
Identifiers
urn:nbn:se:miun:diva-45756 (URN)10.1109/I2MTC48687.2022.9806559 (DOI)000844585400090 ()2-s2.0-85134427845 (Scopus ID)9781665483605 (ISBN)
Conference
2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022, 16 May 2022 through 19 May 2022
Available from: 2022-08-03 Created: 2022-08-03 Last updated: 2024-03-11Bibliographically approved
Jiang, M., Lundgren, J., Pasha, S., Carratù, M., Liguori, C. & Thungström, G. (2020). Indoor Silent Object Localization using Ambient Acoustic Noise Fingerprinting. In: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC): . Paper presented at 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE
Open this publication in new window or tab >>Indoor Silent Object Localization using Ambient Acoustic Noise Fingerprinting
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2020 (English)In: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, 2020Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2020
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-39460 (URN)10.1109/I2MTC43012.2020.9129086 (DOI)2-s2.0-85088298769 (Scopus ID)978-1-7281-4460-3 (ISBN)
Conference
2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
Available from: 2020-07-06 Created: 2020-07-06 Last updated: 2020-08-17Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8253-7535

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