A variety of algorithms intended for the new generation of hearing aids is presented in this thesis. The main contribution of this work is the hidden Markov model (HMM) approach to classifying listening environments. This method is efficient and robust and well suited for hearing aid applications. This thesis shows that several advanced classification methods can be implemented in digital hearing aids with reasonable requirements on memory and calculation resources. A method for analyzing complex hearing aid algorithms is presented. Data from each hearing aid and listening environment is displayed in three different forms: (1) Effective temporal characteristics (Gain-Time), (2) Effective compression characteristics (Input-Output), and (3) Effective frequency response (Insertion Gain). The method works as intended. Changes in the behavior of a hearing aid can be seen under realistic listening conditions. It is possible that the proposed method of analyzing hearing instruments generates too much information for the user. An automatic gain controlled (AGC) hearing aid algorithm adapting to two sound sources in the listening environment is presented. The main idea of this algorithm is to: (1) adapt slowly (in approximately 10 seconds) to varying listening environments, e.g. when the user leaves a disciplined conference for a multi-babble coffee-break; (2) switch rapidly(in about 100 ms) between different dominant sound sources within one listening situation, such as the change from the user's own voice to a distant speaker's voice in a quiet conference room; (3) instantly reduce gain for strong transient sounds and then quickly return to the previous gain setting; and (4) not change the gain in silent pauses but instead keep the gain setting of the previous sound source. An acoustic evaluation shows that the algorithm works as intended. A system for listening environment classification in hearing aids is also presented. The task is to automatically classify three different listening environments: 'speech in quiet', 'speech in traffic', and 'speech in babble'. The study shows that the three listening environments can be robustly classified at a variety of signal-to-noise ratios with only a small set of pre-trained source HMMs. The measured classification hit rate was 96.7-99.5% when the classifier was tested with sounds representing one of the three environment categories included in the classifier. False alarm rates were0.2-1.7% in these tests. The study also shows that the system can be implemented with the available resources in today's digital hearing aids. Another implementation of the classifier shows that it is possible to automatically detect when the person wearing the hearing aid uses the telephone. It is demonstrated that future hearing aids may be able to distinguish between the sound of a face-to-face conversation and a telephone conversation, both in noisy and quiet surroundings. However, this classification algorithm alone may not be fast enough to prevent initial feedback problems when the user places the telephone handset at the ear. A method using the classifier result for estimating signal and noise spectra for different listening environments is presented. This evaluation shows that it is possible to robustly estimate signal and noise spectra given that the classifier has good performance. An implementation and an evaluation of a single keyword recognizer for a hearing instrument are presented. The performance for the best parameter setting gives 7e-5 [1/s] in false alarm rate, i.e. one false alarm for every four hours of continuous speech from the user, 100% hit rate for an indoors quiet environment, 71% hit rate for an outdoors/traffic environment and 50% hit rate for a babble noise environment. The memory resource needed for the implemented system is estimated to 1820 words (16-bits). Optimization of the algorithm together with improved technology will inevitably make it possible to implement the system in a digital hearing aid within the next couple of years. A solution to extend the number of keywords and integrate the system with a sound environment classifier is also outlined. / QC 20100611

Context. Environmental Sound Recognition, unlike Speech Recognition, is an area that is still in the developing stages with respect to using Deep Learning methods. Sound can be converted into images by extracting spectrograms and the like. Object Recognition from images using deep Convolutional Neural Networks is a currently developing area holding high promise. The same technique has been studied and applied, but on image representations of sound. Objectives. In this study, investigation is done to determine the best possible accuracy of performing a sound classification task using existing deep Convolutional Neural Networks by comparing the data pre-processing parameters. Also, a novel method of combining different features into a single image is proposed and its effect tested. Lastly, the performance of an existing network that fuses Convolutional and Recurrent Neural architectures is tested on the selected datasets. Methods. In this, experiments were conducted to analyze the effects of data pre-processing parameters on the best possible accuracy with two CNNs. Also, experiment was also conducted to determine whether the proposed method of feature combination is beneficial or not. Finally, an experiment to test the performance of a combined network was conducted. Results. GoogLeNet had the highest classification accuracy of 73% on 50-class dataset and 90-93% on 10-class datasets. The sampling rate and frame length values of the respective datasets which contributed to the high scores are 16kHz, 40ms and 8kHz, 50ms respectively. The proposed combination of features does not improve the classification accuracy. The fused CRNN network could not achieve high accuracy on the selected datasets. Conclusions. It is concluded that deep networks designed for object recognition can be successfully used to classify environmental sounds and the pre-processing parameters’ values determined for achieving best accuracy. The novel method of feature combination does not significantly improve the accuracy when compared to spectrograms alone. The fused network which learns the special and temporal features from spectral images performs poorly in the classification task when compared to the convolutional network alone.

Having enough data about the usage of tyre types on the road can lead to a better understanding of the consequences of studded tyres on the environment. This paper is focused on training and testing a machine learning model which can be further integrated into a larger system for automation of the data collection process. Different machine learning algorithms, namely CNN, SVM, and Random Forest, were compared in this experiment. The method used in this paper is an empirical method. First, sound data for studded and none-studded tyres was collected from three different locations in the city of Gävle/Sweden. A total of 760 Mel spectrograms from both classes was generated to train and test a well-known CNN model (AlexNet) on MATLAB. Sound features for both classes were extracted using JAudio to train and test models that use SVM and Random Forest classifi-ers on Weka. Unnecessary features were removed one by one from the list of features to improve the performance of the classifiers. The result shows that CNN achieved accuracy of 84%, SVM has the best performance both with and without removing some audio features (i.e 94% and 92%, respectively), while Random Forest has 89 % accuracy. The test data is comprised of 51% of the studded class and 49% of the none-studded class and the result of the SVM model has achieved more than 94 %. Therefore, it can be considered as an acceptable result that can be used in practice.

Artificial neural networks have in the last decade been a vital tool in image recognition, signal processing and speech recognition. Because of these networks' ability to be highly flexible, they suit a vast amount of different data. This flexible attribute is very sought for within the field of environmental sound classification. This thesis seeks to investigate if audio from three types of water usage can be distinguished and classified. The usage types investigated are handwashing, showering and WC-flushing. The data originally consisted of sound recordings in WAV format. The recordings were converted into spectrograms, which are visual representations of audio signals. Two neural networks are addressed for this image classification issue, namely a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN). Further, these spectrograms are subject to both image preprocessing using a Sobel filter, a Canny edge detector and a Gabor filter while also being subjected to data augmentation by applying different brightness and zooming alterations. The result showed that the CNN gave superior results compared to the MLP. The image preprocessing techniques did not improve the data and the model performances, neither did augmentation or a combination between them. An important finding was that constructing the convolutional and pooling filters of the CNN into rectangular shapes and using every other filter type horizontally and vertically on the input spectrogram gave superior results. It seemed to capture more information of the spectrograms since spectrograms mainly contain information in a horizontal or vertical direction. This model achieved 91.14% accuracy. The result stemming from this model architecture  further contributes to the environmental sound classification community. / <p>Masters thesis approved 20th june 2022.</p>

Evaluation of the road's condition and state is essential for its upkeep, especially when discussing gravel roads, for the following reasons, among other. When loose gravel is not adequately maintained, it can pose a hazard to drivers, who can lose control of their vehicle and cause accidents. Current maintenance procedures are either laborious or time-consuming. Road agencies and institutions are on the lookout for more effective techniques. This study seeks to establish an automatic method for estimating loose gravel using acoustic observation. On gravelroads, recordings from a car's interior were evaluated and matched to the road's state. The first strategy examined road sections with a four-tier (multiclass) manual classification, based on their perceived condition of loose gravel, in accordance with the Swedish road administration authority’s guidelines. The second, examined two tier (binary) manual classification, distinguishing between roads with low and high maintenance needs. Sound features were extracted and processed for subsequentanalysis. Several supervised machine learning methods and algorithms, combined with selected data preprocessing strategies, were deployed. The performance of each strategy and model is determined by assessing and evaluating their classification accuracy along with other performance metrics. The SVM classifier had the best performance in classifying both multiclass as well as binary gravel road conditions. SVM achieved an accuracy of 57.8% when classifying on a four-tier scale and an accuracy of 82% when classifying on a two-tier scale. These results indicate some merits of using audio features as predictive features in the automatic classification of loose gravel conditions on gravel roads.

<div> The Unmanned Aerial Vehicles (UAV) technology has evolved exponentially in recent years. Smaller and less expensive devices allow a world of new applications in different areas, but as this progress can be beneficial, the use of UAVs with malicious intentions also poses a threat. UAVs can carry weapons or explosives and access restricted zones passing undetected, representing a real threat for civilians and institutions. Acoustic detection in combination with machine learning models emerges as a viable solution since, despite its limitations related with environmental noise, it has provided promising results on classifying UAV sounds, it is adaptable to multiple environments, and especially, it can be a cost-effective solution, something much needed in the counter UAV market with high projections for the coming years. The problem addressed by this project is the need for a real-world adaptable solution which can show that an array of acoustic sensors can be implemented for the detection and localization of UAVs with minimal cost and competitive performance.<br><br></div><div> In this research, a low-cost acoustic detection system that can detect, in real time, about the presence and direction of arrival of a UAV approaching a target was engineered and validated. The model developed includes an array of acoustic sensors remotely connected to a central server, which uses the sound signals to estimate the direction of arrival of the UAV. This model works with a single microphone per node which calculates the position based on the acoustic intensity change produced by the UAV, reducing the implementation costs and being able to work asynchronously. The development of the project included collecting data from UAVs flying both indoors and outdoors, and a performance analysis under realistic conditions. <br><br></div><div> The results demonstrated that the solution provides real time UAV detection and localization information to protect a target from an attacking UAV, and that it can be applied in real world scenarios. </div><div><br></div>

A malha urbana das grandes cidades tem sido ocupada, principalmente nos grandes centros, por vários edifícios corporativos, localizados em pólos administrativos onde os ruídos urbanos são constantes. Além dos ruídos externos a que o edifício fica exposto, existem também os ruídos internos, ambos impactando no conforto e produtividade de seus ocupantes, trazendo problemas para a empresa. Tendo a situação brevemente descrita acima como preocupante, este trabalho tem por objetivo geral, investigar quais materiais industrializados para tratamento acústico e quais técnicas são utilizadas, para redução e controle, da propagação de ruídos aéreos em edifícios de escritórios, nos últimos dez anos. Incluem-se assim o estudo e apresentação das características técnicas para especificação de forros acústicos, barreiras acústicas, tratamento de paredes, pisos, layout e mobiliário, vidros acústicos, tratamento dos ruídos gerados pelo ar condicionado e mascaramento sonoro. O objetivo específico neste trabalho é analisar os materiais utilizados para tratamento acústico, baseado nos índices de classificação acústica, seja este de absorção ou isolamento, com relação ao desempenho do material. São eles: NRC (Índice de Redução Sonora), \'alfa\' (Coeficiente de Absorção), \'alfa\'w (Coeficiente de Absorção Sonora Ponderado) Rw (Índice de Redução Acústica) e STC (Classe de Transmissão Sonora). Esta pesquisa tem como resultado a centralização de dados técnicos referente à utilização de materiais acústicos para controle de ruídos aéreos em edifícios de escritórios; a análise das classificações e desempenhos e a avaliação da disponibilização de informações técnicas nos catálogos brasileiros de produtos. Tal resultado oferecerá uma base de consulta para obtenção de critérios durante a especificação e elaboração de projetos de espaços corporativos. O trabalho conclui apontando observações da autora, referentes à utilização dos materiais e técnicas e a necessidade de criação e produção de um maior portfólio de produtos. / Big cities have been taken the ground thought freeways and avenues, where constant and loud noise takes place. At theses ways, corporate edifices are also often established, so they naturally become exposed to the external noise interference, regardless the own internal facilities noising. The consequence is that people how work at these places are more prone to be less productive, turning it into a drawback to the corporate. The scenario described above is very concerning. This general research objective is to study techniques and industrialized material in order to reduce and control the noise propagation thought the air on for corporate facilities, focused at the last ten years time frame. It will focus on study and present the technical characteristics for acoustic absorption and/or isolation of roof lining, acoustic barriers, walls, floor, office layout, furniture, glasses, air-conditioner, and sound masking techniques. The specific research objective is to analyze the materials for acoustic handling, based on acoustic classification indexes that relate to the material performance, for absorption and isolation, as listed: NRC (Noise Reduction Coefficient), \'alfa\' (Sound Absorption Coefficient), \'alfa\'w (Pounder Sound Absorption Coefficient), Rw (Noise Reduction Index) and STC (Sound Transmission Class). The objective of this research is to be a single point of reference for technical data regarding the utilization of materials that can be used to control the sound propagation thought the air on corporate edifices; the classification of analysis and performance as well as the evaluation of technical information available on Brazilian product catalogs. The research result offers a baseline for those who look for criteria during the speciation and implementation of corporative spaces. The conclusion of this work is done with the Authors considerations regarding the utilization of materials and techniques, as well the necessity of creation and production of wider product portfolio.

Στο κεφάλαιο 1 παρουσιάζεται μία γενική επισκόπηση της αυτόματης αναγνώρισης γενικευμένων ακουστικών γεγονότων. Επιπλέον συζητάμε τις εφαρμογές της τεχνολογίας αναγνώρισης ακουστικού σήματος και δίνουμε μία σύντομη περιγραφή του state of the art. Τέλος, αναφέρουμε τη συνεισφορά της διατριβής. Στο κεφάλαιο 2 εισάγουμε τον αναγνώστη στο χώρο της επεξεργασίας ακουστικών σημάτων που δε περιλαμβάνουν ομιλία. Παρουσιάζονται οι σύγχρονες προσεγγίσεις όσον αφορά στις μεθοδολογίες εξαγωγής χαρακτηριστικών και αναγνώρισης προτύπων. Στο κεφάλαιο 3 προτείνεται ένα καινοτόμο σύστημα αναγνώρισης ήχων ειδικά σχεδιασμένο για το χώρο των ηχητικών γεγονότων αστικού περιβάλλοντος και αναλύεται ο σχεδιασμός της αντίστοιχης βάσης δεδομένων. Δημιουργήθηκε μία ιεραρχική πιθανοτική δομή μαζί με δύο ομάδες ακουστικών παραμέτρων που οδηγούν σε υψηλή ακρίβεια αναγνώρισης. Στο κεφάλαιο 4 ερευνάται η χρήση της τεχνικής πολλαπλών αναλύσεων όπως εφαρμόζεται στο πρόβλημα της διάκρισης ομιλίας/μουσικής. Στη συνέχεια η τεχνική αυτή χρησιμοποιήθηκε για τη δημιουργία ενός συστήματος το οποίο συνδυάζει χαρακτηριστικά από διαφορετικά πεδία με στόχο την αποδοτική ανάλυση online ραδιοφωνικών σημάτων. Στο κεφάλαιο 5 προτείνεται ένα σύστημα το οποίο εντοπίζει μη-τυπικές καταστάσεις σε περιβάλλον σταθμού μετρό με στόχο να βοηθήσει το εξουσιοδοτημένο προσωπικό στην συνεχή επίβλεψη του χώρου. Στο κεφάλαιο 6 προτείνεται ένα προσαρμοζόμενο σύστημα για ακουστική παρακολούθηση εν δυνάμει καταστροφικών καταστάσεων ικανό να λειτουργεί κάτω από διαφορετικά περιβάλλοντα. Δείχνουμε ότι το σύστημα επιτυγχάνει υψηλή απόδοση και μπορεί να προσαρμόζεται αυτόνομα σε ετερογενείς ακουστικές συνθήκες. Στο κεφάλαιο 7 ερευνάται η χρήση της μεθόδου ανίχνευσης καινοτομίας για ακουστική επόπτευση κλειστών και ανοιχτών χώρων. Ηχογραφήθηκε μία βάση δεδομένων πραγματικού κόσμου και προτείνονται τρεις πιθανοτικές τεχνικές. Στο κεφάλαιο 8 παρουσιάζεται μία καινοτόμα μεθοδολογία για αναγνώριση γενικευμένου ακουστικού σήματος που οδηγεί σε υψηλή ακρίβεια αναγνώρισης. Εκμεταλλευόμαστε τα πλεονεκτήματα της χρονικής συγχώνευσης χαρακτηριστικών σε συνδυασμό με μία παραγωγική τεχνική κατηγοριοποίησης. / The dissertation is outlined as followed: In chapter 1 we present a general overview of the task of automatic recognition of sound events. Additionally we discuss the applications of the generalized audio signal recognition technology and we give a brief description of the state of the art. Finally we mention the contribution of the thesis. In chapter 2 we introduce the reader to the area of non speech audio processing. We provide the current trend in the feature extraction methodologies as well as the pattern recognition techniques. In chapter 3 we analyze a novel sound recognition system especially designed for addressing the domain of urban environmental sound events. A hierarchical probabilistic structure was constructed along with a combined set of sound parameters which lead to high accuracy. chapter 4 is divided in the following two parts: a) we explore the usage of multiresolution analysis as regards the speech/music discrimination problem and b) the previously acquired knowledge was used to build a system which combined features of different domains towards efficient analysis of online radio signals. In chapter 5 we exhaustively experiment on a new application of the sound recognition technology, space monitoring based on the acoustic modality. We propose a system which detects atypical situations under a metro station environment towards assisting the authorized personnel in the space monitoring task. In chapter 6 we propose an adaptive framework for acoustic surveillance of potentially hazardous situations under environments of different acoustic properties. We show that the system achieves high performance and has the ability to adapt to heterogeneous environments in an unsupervised way. In chapter 7 we investigate the usage of the novelty detection method to the task of acoustic monitoring of indoor and outdoor spaces. A database with real-world data was recorded and three probabilistic techniques are proposed. In chapter 8 we present a novel methodology for generalized sound recognition that leads to high recognition accuracy. The merits of temporal feature integration as well as multi domain descriptors are exploited in combination with a state of the art generative classification technique.

A malha urbana das grandes cidades tem sido ocupada, principalmente nos grandes centros, por vários edifícios corporativos, localizados em pólos administrativos onde os ruídos urbanos são constantes. Além dos ruídos externos a que o edifício fica exposto, existem também os ruídos internos, ambos impactando no conforto e produtividade de seus ocupantes, trazendo problemas para a empresa. Tendo a situação brevemente descrita acima como preocupante, este trabalho tem por objetivo geral, investigar quais materiais industrializados para tratamento acústico e quais técnicas são utilizadas, para redução e controle, da propagação de ruídos aéreos em edifícios de escritórios, nos últimos dez anos. Incluem-se assim o estudo e apresentação das características técnicas para especificação de forros acústicos, barreiras acústicas, tratamento de paredes, pisos, layout e mobiliário, vidros acústicos, tratamento dos ruídos gerados pelo ar condicionado e mascaramento sonoro. O objetivo específico neste trabalho é analisar os materiais utilizados para tratamento acústico, baseado nos índices de classificação acústica, seja este de absorção ou isolamento, com relação ao desempenho do material. São eles: NRC (Índice de Redução Sonora), \'alfa\' (Coeficiente de Absorção), \'alfa\'w (Coeficiente de Absorção Sonora Ponderado) Rw (Índice de Redução Acústica) e STC (Classe de Transmissão Sonora). Esta pesquisa tem como resultado a centralização de dados técnicos referente à utilização de materiais acústicos para controle de ruídos aéreos em edifícios de escritórios; a análise das classificações e desempenhos e a avaliação da disponibilização de informações técnicas nos catálogos brasileiros de produtos. Tal resultado oferecerá uma base de consulta para obtenção de critérios durante a especificação e elaboração de projetos de espaços corporativos. O trabalho conclui apontando observações da autora, referentes à utilização dos materiais e técnicas e a necessidade de criação e produção de um maior portfólio de produtos. / Big cities have been taken the ground thought freeways and avenues, where constant and loud noise takes place. At theses ways, corporate edifices are also often established, so they naturally become exposed to the external noise interference, regardless the own internal facilities noising. The consequence is that people how work at these places are more prone to be less productive, turning it into a drawback to the corporate. The scenario described above is very concerning. This general research objective is to study techniques and industrialized material in order to reduce and control the noise propagation thought the air on for corporate facilities, focused at the last ten years time frame. It will focus on study and present the technical characteristics for acoustic absorption and/or isolation of roof lining, acoustic barriers, walls, floor, office layout, furniture, glasses, air-conditioner, and sound masking techniques. The specific research objective is to analyze the materials for acoustic handling, based on acoustic classification indexes that relate to the material performance, for absorption and isolation, as listed: NRC (Noise Reduction Coefficient), \'alfa\' (Sound Absorption Coefficient), \'alfa\'w (Pounder Sound Absorption Coefficient), Rw (Noise Reduction Index) and STC (Sound Transmission Class). The objective of this research is to be a single point of reference for technical data regarding the utilization of materials that can be used to control the sound propagation thought the air on corporate edifices; the classification of analysis and performance as well as the evaluation of technical information available on Brazilian product catalogs. The research result offers a baseline for those who look for criteria during the speciation and implementation of corporative spaces. The conclusion of this work is done with the Authors considerations regarding the utilization of materials and techniques, as well the necessity of creation and production of wider product portfolio.