Papers by Leandro Vignolo
arXiv (Cornell University), Dec 2, 2023
• A review on monitoring methodologies of ruminant feeding behaviour is presented. • Advantages a... more • A review on monitoring methodologies of ruminant feeding behaviour is presented. • Advantages and disadvantages of the available sensing methodologies are discussed. • Features of the acquisition, management, and availability of the data are discussed. • Analysis of the signal processing and machine learning methods used in the algorithm. • Challenges and future research directions in the area are discussed.
ABSTRACTMonitoring livestock feeding behavior may help assess animal welfare and nutritional stat... more ABSTRACTMonitoring livestock feeding behavior may help assess animal welfare and nutritional status, and to optimize pasture management. The need for continuous and sustained monitoring requires the use of automatic techniques based on the acquisition and analysis of sensor data. This work describes an open dataset of acoustic recordings of the foraging behavior of dairy cows. The dataset includes 662 h of daily records obtained using unobtrusive and non-invasive instrumentation mounted on five lactating multiparous Holstein cows continuously monitored for six non-consecutive days in pasture and barn. Labeled recordings precisely delimiting grazing and rumination bouts are provided for a total of 400 h and for over 6,200 ingestive and rumination jaw movements. Companion information on the audio recording quality and expert-generated labels is also provided to facilitate data interpretation and analysis. This comprehensive dataset is a useful resource for studies aimed at exploring n...
Ai Communications, Jul 15, 2019
Speaker state recognition is an important issue to understand the human behaviour and to achieve ... more Speaker state recognition is an important issue to understand the human behaviour and to achieve more comprehensive speech interactive systems, and therefore has received much attention in recent years. This work addresses the automatic classification of three types of child emotions in vocalisations: neutral mood, fussing (negative mood) and crying (negative mood). Speech, in a broad sense, contains a lot of para-linguistic information that can be revealed by means of different methods for feature extraction and, in this case, these would be useful for mood detection. Here, several set of features are proposed, combined and compared with state-of-art characteristics used for speech-related tasks, and these are based on spectral information, bio-inspired ear model, auditory sparse representations with dictionaries, optimised wavelet coefficients and optimised filter bank for cepstral representation. All the experiments were performed using the Extreme Learning Machines as classifier because it is a state-of-art classifier and to achieve comparable results. The results show that by means of the proposed feature extraction methods it is possible to improve the performance provided by the baseline features. Also, different combinations of the developed feature sets were studied in order to further exploit their properties.
The automatic classification of calling bird species is important to achieve more exhaustive envi... more The automatic classification of calling bird species is important to achieve more exhaustive environmental monitoring and to manage natural resources. Bird vocalizations allow to recognise new species, their natural history and macro-systematic relations, while automatic systems can speed up and improve all the process. In this work, we use state-of-art features designed for speech and speaker state recognition to classify 25 species of Furnariidae family. Since Furnariidae species inhabit the Litoral Paranaense region of Argentina (South America), this work could promote further research on the topic and the implementation of in-situ monitoring systems. Our analysis includes two widely-known classification techniques: random forest an support vector machines. The results are promising, near 86%, and were validated in a cross-validation scheme.
Lecture Notes in Computer Science, 2018
This work deals with the development of features for the automatic classification of infant cry, ... more This work deals with the development of features for the automatic classification of infant cry, considering three categories: neutral, fussing and crying vocalisations. Mel-frequency cepstral coefficients, together with standard functional obtained from these, have long been the most widely used features for all kind of speech-related tasks, including infant cry classification. However, recent works have introduced alternative filter banks leading to performance improvements and increased robustness. In this work, the optimisation of a filter bank is proposed for feature extraction and two other spectrum-based feature sets are compared. The first set of features is obtained through the optimisation of filter banks, by means of an evolutionary algorithm, in order to find a more suitable speech representation for the infant cry classification. Moreover, the classification performance of the optimised representation combined with other spectral features based on the mean log-spectrum and auditory spectrum is evaluated. The results show that these feature sets are able to improve the performance for the cry classification task.
2017 XLIII Latin American Computer Conference (CLEI), Sep 1, 2017
Feature selection is an extremely important matter in pattern recognition, particularly when a la... more Feature selection is an extremely important matter in pattern recognition, particularly when a large set of features is available without knowledge about the discriminative information provided by each element. The key issue is to define a criterion in order to rank the features, discarding those features that are less relevant, redundant, or noisy. This depends on the particular task, the classifier and the properties of the data. A frequent approach consists on the use of genetic algorithms guided by the classification accuracy. However they are often not able to provide a solution with both a considerable reduction of dimensionality and high accuracy rate. Here we propose a modified version of a genetic algorithm, introducing a novel local improvement approach based on evolution, which is able to obtain better dimensionality-accuracy trade-off. Experimental results on different well known datasets show the advantages of our proposal.
Active shape models is an adaptive shape-matching technique that has been used for locating facia... more Active shape models is an adaptive shape-matching technique that has been used for locating facial features in images. However, when a number of features is extracted for each landmark point, distortions caused by noise or illumination, and the dimensionality of the final representation, have a negative impact in the performance of a classifier. In this paper, an evolutionary wrapper for selection of the most relevant set of features for face recognition is presented. The proposed strategy explores the space of multiple feasible selections using genetic algorithms. Experimental results show that the proposed approach allows to improve the classification performance in comparison with another enhanced method and a state of the art face recognition approach.
Automatic bird species classification and identification are issues that have aroused interest in... more Automatic bird species classification and identification are issues that have aroused interest in recent years. The main goals involve more exhaustive environmental monitoring and natural resources managing. One of the more relevant characteristics of calling birds is the vocalisation because this allows to recognise species or identify new ones, to know its natural history and macro-systematic relations, among others. In this work, some spectral-based features and extreme learning machines (ELM) are used to perform bird species classification. The experiments were carried on using 25 species of the family Furnariidae that inhabit the Paranaense Littoral region of Argentina (South America) and were validated in a cross-validation scheme. The results show that ELM classifier obtains high classification rates, more than 90% in accuracy, and the proposed features overperform the baseline features.
The pervasive development disorders in autism condition lead to impairments in language and socia... more The pervasive development disorders in autism condition lead to impairments in language and social communication. They are evidenced as atypical prosody production, emotion recognition and apraxia, among others communication deficits. This work tackle with the problem of the recognition of pathologies derived from these disorders in children, based on the acoustic analysis of speech. Specifically, the task consists of the diagnosis of normality (typically developing children) or three different pathologies. We propose an evolutionary approach to the feature selection stage. It relies on the use of genetic algorithm to find the set of features that optimally represent the speech data for this classification task. The genetic algorithm uses a support vector machine in order to evaluate the solutions (each individual) during the search. The results showed that our methodology improves the baseline provided for the task. The obtained unweighted classification accuracy was 54.80% on the development set, which represents a relative improvement of 6%, and 55.41% on test set. On the related task of binary classification between typical versus atypical developing condition, our approach achieved an unweighted classification accuracy of 92.66% on the test set.
Engineering Applications of Artificial Intelligence
Social Science Research Network, 2022
2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI), 2013
The pervasive development disorders in autism condition lead to impairments in language and socia... more The pervasive development disorders in autism condition lead to impairments in language and social communication. They are evidenced as atypical prosody production, emotion recognition and apraxia, among others communication deficits. This work tackle with the problem of the recognition of pathologies derived from these disorders in children, based on the acoustic analysis of speech. Specifically, the task consists of the diagnosis of normality (typically developing children) or three different pathologies. We propose an evolutionary approach to the feature selection stage. It relies on the use of genetic algorithm to find the set of features that optimally represent the speech data for this classification task. The genetic algorithm uses a support vector machine in order to evaluate the solutions (each individual) during the search. The results showed that our methodology improves the baseline provided for the task. The obtained unweighted classification accuracy was 54.80% on the development set, which represents a relative improvement of 6%, and 55.41% on test set. On the related task of binary classification between typical versus atypical developing condition, our approach achieved an unweighted classification accuracy of 92.66% on the test set.
Expert Systems with Applications, 2013
Feature selection is a key issue in pattern recognition, specially when prior knowledge of the mo... more Feature selection is a key issue in pattern recognition, specially when prior knowledge of the most discriminant features is not available. Moreover, in order to perform the classification task with reduced complexity and acceptable performance, usually features that are irrelevant, redundant, or noisy are excluded from the problem representation. This work presents a multiobjective wrapper, based on genetic algorithms, to select the most relevant set of features for face recognition tasks. The proposed strategy explores the space of multiple feasible selections in order to minimize the cardinality of the feature subset, and at the same time to maximize its discriminative capacity. Experimental results show that, in comparison with other state-of-the-art approaches, the proposed approach allows to improve the classification performance, while reducing the representation dimensionality.
Feature selection is an extremely important matter in pattern recognition, particularly when a la... more Feature selection is an extremely important matter in pattern recognition, particularly when a large set of features is available without knowledge about the discriminative information provided by each element. The key issue is to define a criterion in order to rank the features, discarding those features that are less relevant, redundant, or noisy. This depends on the particular task, the classifier and the properties of the data. A frequent approach consists on the use of genetic algorithms guided by the classification accuracy. However they are often not able to provide a solution with both a considerable reduction of dimensionality and high accuracy rate. Here we propose a modified version of a genetic algorithm, introducing a novel local improvement approach based on evolution, which is able to obtain better dimensionality-accuracy trade-off. Experimental results on different well known datasets show the advantages of our proposal.
The automatic classification of calling bird species is important to achieve more exhaustive envi... more The automatic classification of calling bird species is important to achieve more exhaustive environmental monitoring and to manage natural resources. Bird vocalizations allow to recognise new species, their natural history and macro-systematic relations, while automatic systems can speed up and improve all the process. In this work, we use state-of-art features designed for speech and speaker state recognition to classify 25 species of Furnariidae family. Since Furnariidae species inhabit the Litoral Paranaense region of Argentina (South America), this work could promote further research on the topic and the implementation of in-situ monitoring systems. Our analysis includes two widely-known classification techniques: random forest an support vector machines. The results are promising, near 86%, and were validated in a cross-validation scheme.
This work deals with the development of features for the automatic classification of infant cry, ... more This work deals with the development of features for the automatic classification of infant cry, considering three categories: neutral, fussing and crying vocalisations. Mel-frequency cepstral coefficients, together with standard functional obtained from these, have long been the most widely used features for all kind of speech-related tasks, including infant cry classification. However, recent works have introduced alternative filter banks leading to performance improvements and increased robustness. In this work, the optimisation of a filter bank is proposed for feature extraction and two other spectrum-based feature sets are compared. The first set of features is obtained through the optimisation of filter banks, by means of an evolutionary algorithm, in order to find a more suitable speech representation for the infant cry classification. Moreover, the classification performance of the optimised representation combined with other spectral features based on the mean log-spectrum ...
Ecological Informatics
A B S T R A C T Over the last years, researchers have addressed the automatic classification of c... more A B S T R A C T Over the last years, researchers have addressed the automatic classification of calling bird species. This is important for achieving more exhaustive environmental monitoring and for managing natural resources. Vocalisations help to identify new species, their natural history and macro-systematic relations, while computer systems allow the bird recognition process to be sped up and improved. In this study, an approach that uses state-of-the-art features designed for speech and speaker state recognition is presented. A method for voice activity detection was employed previous to feature extraction. Our analysis includes several classification techniques (multilayer perceptrons, support vector machines and random forest) and compares their performance using different configurations to define the best classification method. The experimental results were validated in a cross-validation scheme, using 25 species of the family Furnariidae that inhabit the Paranaense Littoral region of Argentina (South America). The results show that a high classification rate, close to 90%, is obtained for this family in this Furnariidae group using the proposed features and classifiers.
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Papers by Leandro Vignolo