IEEE transactions on emerging topics in computational intelligence, Feb 1, 2021
In the recent years, several supervised and unsupervised approaches to fall detection have been p... more In the recent years, several supervised and unsupervised approaches to fall detection have been presented in the literature. These are generally based on a corpus of examples of human falls that are, though, hard to collect. For this reason, fall detection algorithms should be designed to gather as much information as possible from the few available data related to the type of events to be detected. The one-shot learning paradigm for expert systems training seems to naturally match these constraints, and this inspired the novel Siamese Neural Network (SNN) architecture for human fall detection proposed in this contribution. Acoustic data are employed as input, and the twin convolutional autoencoders composing the SNN are trained to perform a suitable metric learning in the audio domain and, thus, extract robust features to be used in the final classification stage. A large acoustic dataset has been recorded in three real rooms with different floor types and human falls performed by four volunteers, and then adopted for experiments. Obtained results show that the proposed approach, which only relies on two real human fall events in the training phase, achieves a F$_1$-Measure of 93.58% during testing, remarkably outperforming the recent supervised and unsupervised state-of-art techniques selected for comparison.
2018 26th European Signal Processing Conference (EUSIPCO), 2018
In this paper, we propose a system for rare sound event detection using a hierarchical and multi-... more In this paper, we propose a system for rare sound event detection using a hierarchical and multi-scaled approach based on Convolutional Neural Networks (CNN). The task consists on detection of event onsets from artificially generated mixtures. Spectral features are extracted from frames of the acoustic signals, then a first event detection stage operates as binary classifier at frame-rate and it proposes to the second stage contiguous blocks of frames which are assumed to contain a sound event. The second stage refines the event detection of the prior network, discarding blocks that contain background sounds wrongly classified by the first stage. Finally, the effective onset time of the active event is obtained. The performance of the algorithm has been assessed with the material provided for the second task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2017. The achieved overall error rate and F-measure, resulting respectively equal to 0.22 and 88.50% on the evaluation dataset, significantly outperforms the challenge baseline and the system guarantees improved generalization performance with a reduced number of free network parameters w.r.t. other competitive algorithms.
Quantifying and Processing Biomedical and Behavioral Signals, 2018
América Latina vive el incremento de la conflictividad socioambiental producto del establecimient... more América Latina vive el incremento de la conflictividad socioambiental producto del establecimiento de megaproyectos de distinto tipo. Este proceso, ocurrido en el marco de una "reprimarización de la economía" y de una estrategia de "apropiación geopolítica y nuevo imperialismo", ha generado la emergencia de movimientos por la defensa del territorio. En Centroamérica hay alrededor de 81 conflictos socioambientales y en cada caso hay una serie de organizaciones, en su mayoría indígenas, que se han conformado alrededor de la defensa del territorio, con anclajes y estrategias de lucha diversas. ¿Representan estos movimientos en su conjunto la rearticulación de la movilización social y política en la región luego de años de "pacificación"? Proponemos una mirada desde la propuesta de los procesos de subjetivación política para abordar esta pregunta. Palabras clave: movimientos por la defensa del territorio, Centroamérica, subjetividades políticas.
Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence - PRAI 2018, 2018
Nowadays, the detection of human fall is a problem recognized by the entire scientific community.... more Nowadays, the detection of human fall is a problem recognized by the entire scientific community. Methods that have good performance use human falls samples in the train set, while methods that do not use it, can only work well under certain conditions. Since examples of human falls are very difficult to retrieve, there is a strong need to develop systems that can work well event with few or no data to be used for their training phase. In this article, we show a first study on few-shot learning Siamese Neural Network applied to human falls detection by using audio signals. This method has been compared with algorithms based on SVM and OCSVM, all evaluated starting from the same conditions. The proposed approach is able to learn the differences between signals belonging to different classes of events. In classification phase, using only one human fall signal as a template, it achieves about 80% of F1 -Measure related to the human fall class, while the SVM based method gets around 69%, when it is trained in the same data knowledge conditions.
The primary cause of injury-related death for the elders is represented by falls. The scientific ... more The primary cause of injury-related death for the elders is represented by falls. The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event. The solution proposed in this paper is based on a combined One-Class SVM (OCSVM) and template-matching classifier that discriminate human falls from nonfalls in a semisupervised framework. Acoustic signals are captured by means of a Floor Acoustic Sensor; then Mel-Frequency Cepstral Coefficients and Gaussian Mean Supervectors (GMSs) are extracted for the fall/nonfall discrimination. Here we propose a single-sensor two-stage user-aided approach: in the first stage, the OCSVM detects abnormal acoustic events. In the second, the template-matching classifier produces the final decision exploiting a set of template GMSs related to the events marked as false positives by the user. The performance of the algorithm has been evaluated on a corpus containing human falls and nonfall sounds...
Supporting people in their homes is an important issue both for ethical and practical reasons. In... more Supporting people in their homes is an important issue both for ethical and practical reasons. Indeed, in the recent years, the scientific community devoted particular attention to detecting human falls, since the first cause of death for elderly people is due to the consequences of a fall. In this paper, we propose a human fall classification system based on an innovative floor acoustic sensor able to capture the acoustic waves transmitted through the floor. The algorithm employed is able to discriminate human falls from non falls and it is based on Mel-Frequency Cepstral Coefficients and a two class Support Vector Machine. The dataset employed for performance evaluation is composed by falls of a human mimicking doll, everyday objects and everyday noises. The obtained results show that the proposed solution is suitable for human fall detection in realistic scenarios, allowing to guarantee a 0% miss probability at very low false positive rates.
In the past years, several hybridization techniques have been proposed to synthesize novel audio ... more In the past years, several hybridization techniques have been proposed to synthesize novel audio content owing its properties from two audio sources. These algorithms, however, usually provide no feature learning, leaving the user, often intentionally, exploring parameters by trial-and-error. The introduction of machine learning algorithms in the music processing field calls for an investigation to seek for possible exploitation of their properties such as the ability to learn semantically meaningful features. In this first work, we adopt a Neural Network Autoencoder architecture and we enhance it to exploit temporal dependencies. In our experiments the architecture was able to modify the original timbre, resembling what it learned during the training phase, while preserving the pitch envelope from the input.
We propose a system for rare sound event detection using hierarchical and multi-scaled approach b... more We propose a system for rare sound event detection using hierarchical and multi-scaled approach based on Multi Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). It is our contribution to the rare sound event detection task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2017). The task consists on detection of event onset from artificially generated mixtures. Acoustic features are extracted from the acoustic signals, successively first event detection stage is performed by an MLP based neural network which proposes contiguous blocks of frames to the second stage. The CNN refines the event detection of the prior network, intrinsically operating on a multi-scaled resolution and discarding blocks that contain background wrongly classified by the MLP as event. Finally the effective onset time of the active event is obtained. The achieved overall error rate and F-measure are respectively equal to 0.18 and 90.9% on the developme...
Nowadays, the detection of human fall is a problem recognized by the entire scientific community.... more Nowadays, the detection of human fall is a problem recognized by the entire scientific community. Methods that have good performance use human falls samples in the train set, while methods that do not use it, can only work well under certain conditions. Since examples of human falls are very difficult to retrieve, there is a strong need to develop systems that can work well event with few or no data to be used for their training phase. In this article, we show a first study on few-shot learning Siamese Neural Network applied to human falls detection by using audio signals. This method has been compared with algorithms based on SVM and OCSVM, all evaluated starting from the same conditions. The proposed approach is able to learn the differences between signals belonging to different classes of events. In classification phase, using only one human fall signal as a template, it achieves about 80% of F 1 −Measure related to the human fall class, while the SVM based method gets around 69...
—In this paper, we propose a system for rare sound event detection using a hierarchical and multi... more —In this paper, we propose a system for rare sound event detection using a hierarchical and multi-scaled approach based on Convolutional Neural Networks (CNN). The task consists on detection of event onsets from artificially generated mixtures. Spectral features are extracted from frames of the acoustic signals, then a first event detection stage operates as binary classifier at frame-rate and it proposes to the second stage contiguous blocks of frames which are assumed to contain a sound event. The second stage refines the event detection of the prior network, discarding blocks that contain background sounds wrongly classified by the first stage. Finally, the effective onset time of the active event is obtained. The performance of the algorithm has been assessed with the material provided for the second task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2017. The achieved overall error rate and F-measure, resulting respectively equal to 0.22 and 88.50% on the evaluation dataset, significantly outperforms the challenge baseline and the system guarantees improved generalization performance with a reduced number of free network parameters w.r.t. other competitive algorithms.
Quantifying and Processing Biomedical and Behavioral Signals, 2019
In the past few years,several works describing systems for the prompt detection of falls have bee... more In the past few years,several works describing systems for the prompt detection of falls have been presented in literature. Many of these systems address the problem of fall detection by using some handcrafted features extracted from the input signals.In the meantime interest in the use of feature learning and deep architectures has been increasing, thus reducing the required engineering effort and the need for prior knowledge. A fall detection method based on a Deep Convolutional Neural Network Autoencoder is presented in this work. This method is trained as a novelty detector through the end-to-end strategy. The classifier distinguishes normal sound events generated by common indoor human activity (i.e. footsteps and speech) and music background from novelty sound events produced by human falls. The performance of the algorithm has been assessed on a corpus of fall events created by the authors. Moreover a comparison was made with two different state-of-art algorithms both based on a One Class Support Vector Machine. The results showed an improvement on performance of about 18,2% on average.
In the past years, several hybridization techniques have been proposed to synthesize novel audio ... more In the past years, several hybridization techniques have been proposed to synthesize novel audio content owing its properties from two audio sources. These algorithms, however, usually provide no feature learning, leaving the user, often intentionally, exploring parameters by trial-and-error. The introduction of machine learning algorithms in the music processing field calls for an investigation to seek for possible exploitation of their properties such as the ability to learn semantically meaningful features. In this first work, we adopt a Neural Network Autoencoder architecture and we enhance it to exploit temporal dependencies. In our experiments the architecture was able to modify the original timbre, resembling what it learned during the training phase, while preserving the pitch envelope from the input.
The interest in assistive technologies for supporting people at home is constantly increasing, bo... more The interest in assistive technologies for supporting people at home is constantly increasing, both in academia and industry. In this context, the authors propose a fall classification system based on an innovative acoustic sensor that operates similarly to stethoscopes and captures the acoustic waves transmitted through the floor. The sensor is designed to minimize the impact of aerial sounds in recordings, thus allowing a more focused acoustic description of fall events. The audio signals acquired by means of the sensor are processed by a fall recognition algorithm based on Mel-Frequency Cepstral Coefficients, Supervectors and Support Vector Machines to discriminate among different types of fall events. The performance of the algorithm has been evaluated against a specific audio corpus comprising falls of a human mimicking doll and of everyday objects. The results showed that the floor sensor significantly improves the performance respect to an aerial microphone: in particular, the F1-Measure is 6.50% higher in clean conditions and 8.76% higher in mismatched noisy conditions. The proposed approach, thus, has a considerable advantage over aerial solutions since it is able to achieve higher fall classification performance using a simpler algorithmic pipeline and hardware setup.
The primary cause of injury-related death for the elders is represented by falls. The scientific ... more The primary cause of injury-related death for the elders is represented by falls. The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event. The solution proposed in this paper is based on a combined One-Class SVM (OCSVM) and template-matching classifier that discriminate human falls from nonfalls in a semisupervised framework. Acoustic signals are captured by means of a Floor Acoustic Sensor; then Mel-Frequency Cepstral Coefficients and Gaussian Mean Supervectors (GMSs) are extracted for the fall/nonfall discrimination. Here we propose a single-sensor two-stage user-aided approach: in the first stage, the OCSVM detects abnormal acoustic events. In the second, the template-matching classifier produces the final decision exploiting a set of template GMSs related to the events marked as false positives by the user. The performance of the algorithm has been evaluated on a corpus containing human falls and nonfall sounds. Compared to the OCSVM only approach, the proposed algorithm improves the performance by 10.14% in clean conditions and 4.84% in noisy conditions. Compared to Popescu and Mahnot (2009) the performance improvement is 19.96% in clean conditions and 8.08% in noisy conditions.
IEEE transactions on emerging topics in computational intelligence, Feb 1, 2021
In the recent years, several supervised and unsupervised approaches to fall detection have been p... more In the recent years, several supervised and unsupervised approaches to fall detection have been presented in the literature. These are generally based on a corpus of examples of human falls that are, though, hard to collect. For this reason, fall detection algorithms should be designed to gather as much information as possible from the few available data related to the type of events to be detected. The one-shot learning paradigm for expert systems training seems to naturally match these constraints, and this inspired the novel Siamese Neural Network (SNN) architecture for human fall detection proposed in this contribution. Acoustic data are employed as input, and the twin convolutional autoencoders composing the SNN are trained to perform a suitable metric learning in the audio domain and, thus, extract robust features to be used in the final classification stage. A large acoustic dataset has been recorded in three real rooms with different floor types and human falls performed by four volunteers, and then adopted for experiments. Obtained results show that the proposed approach, which only relies on two real human fall events in the training phase, achieves a F$_1$-Measure of 93.58% during testing, remarkably outperforming the recent supervised and unsupervised state-of-art techniques selected for comparison.
2018 26th European Signal Processing Conference (EUSIPCO), 2018
In this paper, we propose a system for rare sound event detection using a hierarchical and multi-... more In this paper, we propose a system for rare sound event detection using a hierarchical and multi-scaled approach based on Convolutional Neural Networks (CNN). The task consists on detection of event onsets from artificially generated mixtures. Spectral features are extracted from frames of the acoustic signals, then a first event detection stage operates as binary classifier at frame-rate and it proposes to the second stage contiguous blocks of frames which are assumed to contain a sound event. The second stage refines the event detection of the prior network, discarding blocks that contain background sounds wrongly classified by the first stage. Finally, the effective onset time of the active event is obtained. The performance of the algorithm has been assessed with the material provided for the second task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2017. The achieved overall error rate and F-measure, resulting respectively equal to 0.22 and 88.50% on the evaluation dataset, significantly outperforms the challenge baseline and the system guarantees improved generalization performance with a reduced number of free network parameters w.r.t. other competitive algorithms.
Quantifying and Processing Biomedical and Behavioral Signals, 2018
América Latina vive el incremento de la conflictividad socioambiental producto del establecimient... more América Latina vive el incremento de la conflictividad socioambiental producto del establecimiento de megaproyectos de distinto tipo. Este proceso, ocurrido en el marco de una "reprimarización de la economía" y de una estrategia de "apropiación geopolítica y nuevo imperialismo", ha generado la emergencia de movimientos por la defensa del territorio. En Centroamérica hay alrededor de 81 conflictos socioambientales y en cada caso hay una serie de organizaciones, en su mayoría indígenas, que se han conformado alrededor de la defensa del territorio, con anclajes y estrategias de lucha diversas. ¿Representan estos movimientos en su conjunto la rearticulación de la movilización social y política en la región luego de años de "pacificación"? Proponemos una mirada desde la propuesta de los procesos de subjetivación política para abordar esta pregunta. Palabras clave: movimientos por la defensa del territorio, Centroamérica, subjetividades políticas.
Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence - PRAI 2018, 2018
Nowadays, the detection of human fall is a problem recognized by the entire scientific community.... more Nowadays, the detection of human fall is a problem recognized by the entire scientific community. Methods that have good performance use human falls samples in the train set, while methods that do not use it, can only work well under certain conditions. Since examples of human falls are very difficult to retrieve, there is a strong need to develop systems that can work well event with few or no data to be used for their training phase. In this article, we show a first study on few-shot learning Siamese Neural Network applied to human falls detection by using audio signals. This method has been compared with algorithms based on SVM and OCSVM, all evaluated starting from the same conditions. The proposed approach is able to learn the differences between signals belonging to different classes of events. In classification phase, using only one human fall signal as a template, it achieves about 80% of F1 -Measure related to the human fall class, while the SVM based method gets around 69%, when it is trained in the same data knowledge conditions.
The primary cause of injury-related death for the elders is represented by falls. The scientific ... more The primary cause of injury-related death for the elders is represented by falls. The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event. The solution proposed in this paper is based on a combined One-Class SVM (OCSVM) and template-matching classifier that discriminate human falls from nonfalls in a semisupervised framework. Acoustic signals are captured by means of a Floor Acoustic Sensor; then Mel-Frequency Cepstral Coefficients and Gaussian Mean Supervectors (GMSs) are extracted for the fall/nonfall discrimination. Here we propose a single-sensor two-stage user-aided approach: in the first stage, the OCSVM detects abnormal acoustic events. In the second, the template-matching classifier produces the final decision exploiting a set of template GMSs related to the events marked as false positives by the user. The performance of the algorithm has been evaluated on a corpus containing human falls and nonfall sounds...
Supporting people in their homes is an important issue both for ethical and practical reasons. In... more Supporting people in their homes is an important issue both for ethical and practical reasons. Indeed, in the recent years, the scientific community devoted particular attention to detecting human falls, since the first cause of death for elderly people is due to the consequences of a fall. In this paper, we propose a human fall classification system based on an innovative floor acoustic sensor able to capture the acoustic waves transmitted through the floor. The algorithm employed is able to discriminate human falls from non falls and it is based on Mel-Frequency Cepstral Coefficients and a two class Support Vector Machine. The dataset employed for performance evaluation is composed by falls of a human mimicking doll, everyday objects and everyday noises. The obtained results show that the proposed solution is suitable for human fall detection in realistic scenarios, allowing to guarantee a 0% miss probability at very low false positive rates.
In the past years, several hybridization techniques have been proposed to synthesize novel audio ... more In the past years, several hybridization techniques have been proposed to synthesize novel audio content owing its properties from two audio sources. These algorithms, however, usually provide no feature learning, leaving the user, often intentionally, exploring parameters by trial-and-error. The introduction of machine learning algorithms in the music processing field calls for an investigation to seek for possible exploitation of their properties such as the ability to learn semantically meaningful features. In this first work, we adopt a Neural Network Autoencoder architecture and we enhance it to exploit temporal dependencies. In our experiments the architecture was able to modify the original timbre, resembling what it learned during the training phase, while preserving the pitch envelope from the input.
We propose a system for rare sound event detection using hierarchical and multi-scaled approach b... more We propose a system for rare sound event detection using hierarchical and multi-scaled approach based on Multi Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). It is our contribution to the rare sound event detection task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2017). The task consists on detection of event onset from artificially generated mixtures. Acoustic features are extracted from the acoustic signals, successively first event detection stage is performed by an MLP based neural network which proposes contiguous blocks of frames to the second stage. The CNN refines the event detection of the prior network, intrinsically operating on a multi-scaled resolution and discarding blocks that contain background wrongly classified by the MLP as event. Finally the effective onset time of the active event is obtained. The achieved overall error rate and F-measure are respectively equal to 0.18 and 90.9% on the developme...
Nowadays, the detection of human fall is a problem recognized by the entire scientific community.... more Nowadays, the detection of human fall is a problem recognized by the entire scientific community. Methods that have good performance use human falls samples in the train set, while methods that do not use it, can only work well under certain conditions. Since examples of human falls are very difficult to retrieve, there is a strong need to develop systems that can work well event with few or no data to be used for their training phase. In this article, we show a first study on few-shot learning Siamese Neural Network applied to human falls detection by using audio signals. This method has been compared with algorithms based on SVM and OCSVM, all evaluated starting from the same conditions. The proposed approach is able to learn the differences between signals belonging to different classes of events. In classification phase, using only one human fall signal as a template, it achieves about 80% of F 1 −Measure related to the human fall class, while the SVM based method gets around 69...
—In this paper, we propose a system for rare sound event detection using a hierarchical and multi... more —In this paper, we propose a system for rare sound event detection using a hierarchical and multi-scaled approach based on Convolutional Neural Networks (CNN). The task consists on detection of event onsets from artificially generated mixtures. Spectral features are extracted from frames of the acoustic signals, then a first event detection stage operates as binary classifier at frame-rate and it proposes to the second stage contiguous blocks of frames which are assumed to contain a sound event. The second stage refines the event detection of the prior network, discarding blocks that contain background sounds wrongly classified by the first stage. Finally, the effective onset time of the active event is obtained. The performance of the algorithm has been assessed with the material provided for the second task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2017. The achieved overall error rate and F-measure, resulting respectively equal to 0.22 and 88.50% on the evaluation dataset, significantly outperforms the challenge baseline and the system guarantees improved generalization performance with a reduced number of free network parameters w.r.t. other competitive algorithms.
Quantifying and Processing Biomedical and Behavioral Signals, 2019
In the past few years,several works describing systems for the prompt detection of falls have bee... more In the past few years,several works describing systems for the prompt detection of falls have been presented in literature. Many of these systems address the problem of fall detection by using some handcrafted features extracted from the input signals.In the meantime interest in the use of feature learning and deep architectures has been increasing, thus reducing the required engineering effort and the need for prior knowledge. A fall detection method based on a Deep Convolutional Neural Network Autoencoder is presented in this work. This method is trained as a novelty detector through the end-to-end strategy. The classifier distinguishes normal sound events generated by common indoor human activity (i.e. footsteps and speech) and music background from novelty sound events produced by human falls. The performance of the algorithm has been assessed on a corpus of fall events created by the authors. Moreover a comparison was made with two different state-of-art algorithms both based on a One Class Support Vector Machine. The results showed an improvement on performance of about 18,2% on average.
In the past years, several hybridization techniques have been proposed to synthesize novel audio ... more In the past years, several hybridization techniques have been proposed to synthesize novel audio content owing its properties from two audio sources. These algorithms, however, usually provide no feature learning, leaving the user, often intentionally, exploring parameters by trial-and-error. The introduction of machine learning algorithms in the music processing field calls for an investigation to seek for possible exploitation of their properties such as the ability to learn semantically meaningful features. In this first work, we adopt a Neural Network Autoencoder architecture and we enhance it to exploit temporal dependencies. In our experiments the architecture was able to modify the original timbre, resembling what it learned during the training phase, while preserving the pitch envelope from the input.
The interest in assistive technologies for supporting people at home is constantly increasing, bo... more The interest in assistive technologies for supporting people at home is constantly increasing, both in academia and industry. In this context, the authors propose a fall classification system based on an innovative acoustic sensor that operates similarly to stethoscopes and captures the acoustic waves transmitted through the floor. The sensor is designed to minimize the impact of aerial sounds in recordings, thus allowing a more focused acoustic description of fall events. The audio signals acquired by means of the sensor are processed by a fall recognition algorithm based on Mel-Frequency Cepstral Coefficients, Supervectors and Support Vector Machines to discriminate among different types of fall events. The performance of the algorithm has been evaluated against a specific audio corpus comprising falls of a human mimicking doll and of everyday objects. The results showed that the floor sensor significantly improves the performance respect to an aerial microphone: in particular, the F1-Measure is 6.50% higher in clean conditions and 8.76% higher in mismatched noisy conditions. The proposed approach, thus, has a considerable advantage over aerial solutions since it is able to achieve higher fall classification performance using a simpler algorithmic pipeline and hardware setup.
The primary cause of injury-related death for the elders is represented by falls. The scientific ... more The primary cause of injury-related death for the elders is represented by falls. The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event. The solution proposed in this paper is based on a combined One-Class SVM (OCSVM) and template-matching classifier that discriminate human falls from nonfalls in a semisupervised framework. Acoustic signals are captured by means of a Floor Acoustic Sensor; then Mel-Frequency Cepstral Coefficients and Gaussian Mean Supervectors (GMSs) are extracted for the fall/nonfall discrimination. Here we propose a single-sensor two-stage user-aided approach: in the first stage, the OCSVM detects abnormal acoustic events. In the second, the template-matching classifier produces the final decision exploiting a set of template GMSs related to the events marked as false positives by the user. The performance of the algorithm has been evaluated on a corpus containing human falls and nonfall sounds. Compared to the OCSVM only approach, the proposed algorithm improves the performance by 10.14% in clean conditions and 4.84% in noisy conditions. Compared to Popescu and Mahnot (2009) the performance improvement is 19.96% in clean conditions and 8.08% in noisy conditions.
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Papers by Diego Droghini