Temporally-aware algorithms for the classification of anuran sounds

Several authors have shown that the sounds of anurans can be used as an indicator of climate change. Hence, the recording, storage and further processing of a huge number of anuran sounds, distributed over time and space, are required in order to obtain this indicator. Furthermore, it is desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper, six classification methods are proposed, all based on the data-mining domain, which strive to take advantage of the temporal character of the sounds. The definition and comparison of these classification methods is undertaken using several approaches. The main conclusions of this paper are that: (i) the sliding window method attained the best results in the experiments presented, and even outperformed the hidden Markov models usually employed in similar applications; (ii) noteworthy overall classification performance has been obtained, which is an especially striking result considering that the sounds analysed were affected by a highly noisy background; (iii) the instance selection for the determination of the sounds in the training dataset offers better results than cross-validation techniques; and (iv) the temporally-aware classifiers have revealed that they can obtain better performance than their nontemporally-aware counterparts.Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain): excellence eSAPIENS number TIC 570

Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks

The analysis and classification of the sounds produced by certain animal species, notably anurans, have revealed these amphibians to be a potentially strong indicator of temperature fluctuations and therefore of the existence of climate change. Environmental monitoring systems using Wireless Sensor Networks are therefore of interest to obtain indicators of global warming. For the automatic classification of the sounds recorded on such systems, the proper representation of the sound spectrum is essential since it contains the information required for cataloguing anuran calls. The present paper focuses on this process of feature extraction by exploring three alternatives: the standardized MPEG-7, the Filter Bank Energy (FBE), and the Mel Frequency Cepstral Coefficients (MFCC). Moreover, various values for every option in the extraction of spectrum features have been considered. Throughout the paper, it is shown that representing the frame spectrum with pure FBE offers slightly worse results than using the MPEG-7 features. This performance can easily be increased, however, by rescaling the FBE in a double dimension: vertically, by taking the logarithm of the energies; and, horizontally, by applying mel scaling in the filter banks. On the other hand, representing the spectrum in the cepstral domain, as in MFCC, has shown additional marginal improvements in classification performance.University of Seville: Telefónica Chair "Intelligence Networks

Representación óptima del espectro de llamadas de anuros en sistemas de control ambiental utilizando redes de sensores inalámbricos

El análisis y la clasificación de los sonidos producidos por determinadas especies animales, en particular los anuros, han revelado que son un potente indicador de la existencia del cambio climático. Es por lo que los sistemas de monitorización ambiental que utilizan redes de sensores inalámbricos son de gran interés para obtener indicadores del calentamiento global. El presente documento se centra en este proceso de extracción de características explorando tres alternativas: el MPEG- 7 estandarizado, los Filter Bank Energy (Energía de Bancos de Filtros, FBE) y los Mel Frequency Cepstral Coefficients (Coeficientes Cepstrales de Frecuencia de Mel, MFCC). Además, se consideran diferentes valores para cada opción en la extracción de las características del espectro.The analysis and classification of the sounds produced by certain animal species, in particular anurans, have revealed that they are a powerful indicator of the existence of climate change. Environmental monitoring systems using wireless sensor networks are of great interest for global warming indicators. This paper focuses on this feature extraction process by exploring three alternatives: the standardised MPEG-7, Filter Bank Energy (FBE) and Mel Frequency Cepstral Coefficients (MFCC). In addition, different values are considered for each option in the extraction of spectrum characteristics

Improving classification algorithms by considering score series in wireless acoustic sensor networks

The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge interest in urban and wildlife environments where the classification of the different signals has become a major issue. Various algorithms have been described for this purpose, a number of which frame the sound and classify these frames,while others take advantage of the sequential information embedded in a sound signal. In the paper, a new algorithm is proposed that, while maintaining the frame-classification advantages, adds a new phase that considers and classifies the score series derived after frame labelling. These score series are represented using cepstral coefficients and classified using standard machine-learning classifiers. The proposed algorithm has been applied to a dataset of anuran calls and its results compared to the performance obtained in previous experiments on sensor networks. The main outcome of our research is that the consideration of score series strongly outperforms other algorithms and attains outstanding performance despite the noisy background commonly encountered in this kind of application

Aspectos de la simetría de las transformaciones integrales en la caracterización de sonidos

La aplicación de técnicas de aprendizaje automático a señales de sonido requiere la caracterización previa de dichas señales. A menudo, la descripción del espectro sonoro se realiza utilizando coeficientes cepstrales. En este trabajo, se compara el desempeño en la obtención de coeficientes cepstrales mediante dos transformaciones integrales, la Transformación discreta de Fourier (DFT) y la Transformación discreta de coseno (DCT). Se puede demostrar que DCT supera a DFT en la tarea de representar los espectros de sonido. Esta mejora se debe a la simetría del espectro y no a ninguna ventaja intrínseca de DCT. Además, las características de MFCC obtenidas con DCT están notablemente menos correlacionadas que las obtenidas con DFT, lo que hará que las funciones MFCC basadas en DCT sean más potentes en algoritmos de clasificación posteriores

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Methodology for ecosystem change assessing using ecoacoustics analysis

RESUMEN: La ecoacústica se ha convertido en un área de creciente interés para el monitoreo de ecosistemas. Entre las principales ventajas que presenta sobre las técnicas tradicionales se encuentran su bajo costo, poca afectación al entorno y simplicidad; además de que la distribución de varias grabadoras hace posible la recolección de más información. Sin embargo, para estudios de largo plazo, la cantidad de datos hace que la inspección manual de las grabaciones sea una tarea tediosa y por consiguiente el análisis sea limitado. Como alternativa a la inspección manual, una serie de índices han sido propuestos para resumir la información acústica de las grabaciones. No obstante, estos índices han sido aplicados principalmente a estudios de biodiversidad y su relación con el estado del ecosistema no es claro aún. En este trabajo se confió en la robustez del ANOVA frente a datos que no se distribuyen normalmente para proponer una metodología de selección de los mejores índices o descriptores acústicos para una aplicación específica y usarlos para modelar los patrones del paisaje acústico del ecosistema con modelos ocultos de Markov y emisiones por mezclas Gaussianas (GMMHMM). Además, el conjunto de descriptores que entran al modelo incluye por defecto un indicador de biodiversidad para cada banda de 1kHz. Esta metodología fue aplicada a dos casos colombianos con tipos de ecosistema definidos. En el primer caso, una serie de grabaciones de bosque, rastrojo y pastizal fueron colectados por más de un año en el este de Antioquia. La segunda aplicación buscaba encontrar patrones de paisaje acústico de las transformaciones de bosque seco en dos regiones del caribe colombiano. El modelo identificó seis y tres patrones acústicos para la primera y segunda base de datos respectivamente. En la primera aplicación, se encontraron sonidos continuos, alta intensidad biofónica y ocupación de varias bandas en los patrones asociados a bosque, mientras que en los rastrojos se presentó más entropía, que se relaciona con alta presencia geofónica, lo que limita la actividad biofónica. Finalmente los paisajes acústicos de pastizal alternaron entre periodos de alta geofonía y alta complejidad frecuencial, haciéndolo un ecosistema intermedio en el sentido acústico. La adaptación del modelo para clasificación resultó en la identificación del 81% de las muestras de bosque, 96,6 % de las muestras de rastrojo y 51,2 % de las muestras de pastizal. Los resultados de clasificación para la segunda aplicación no fueron altos, con 68% para las muestras de baja transformación, 58,9% para la transformación media y 31,8% para la transformación alta. No obstante, las matrices de confusión indicaron que las muestras de entrenamiento no fueron suficientes, y que debería proporcionarse mayor muestreo para obtener mejores resultados. Dado que GMMHMM es un modelo secuencial, también presentó la configuración temporal de los patrones acústicos dadas sus probabilidades de transición. Esta característica nos permitió destacar la importancia de la conservación, cuando encontramos que los estados más estables e inaccesibles fueron asociados a los ecosistemas más diversos acústicamente.ABSTRACT: Ecoacoustics has become a field of growing interest for ecosystem monitoring. Its main advantages over traditional methods include cost effectiveness, non-invasiveness and simplicity; besides the distribution of many recorder units makes possible the recollection of more information. However, for long term studies, the quantity of collected data makes the manual inspection of recordings a cumbersome task, leading to reduced analysis. As an alternative to manual inspection, a series of indices have been proposed to summarize the acoustical information in recordings. Nonetheless, these indices have been applied mainly to biodiversity studies and their connection to ecosystem state is still not clear. In this work we trusted ANOVA robustness for non-normal data for proposing a methodology that selected the best acoustical indices or features for a specific application and used them to model the ecosystem soundscape patterns with hidden Markov models and Gaussian mixture emissions (GMMHMM). Additionally, the set of input features included by default a biodiversity indicator per 1kHz band. This methodology was applied to two Colombian cases with defined ecosystem types. In the first case, a series of forest, stubble and pasture recordings were collected for over a year in the east of Antioquia. The second application aimed to find the soundscape patterns of dry forests transformations in two regions of the Colombian Caribbean. The model identified six and three soundscape patterns for the first and second dataset respectively. In the first application, continuous sounds, high biophonic intensity and multiple occupied frequency bands were found in the patterns associated to forest sites; on the other hand, stubble sites presented more general entropy, which we related to high geophonic presence, preventing biophonic activity. Lastly, pasture soundscapes alternated between periods of high geophony and high frequency complexity, making it an intermediate ecosystem in the acoustical sense. The adaptation of the model for classification resulted in the identification of 81% of the forest samples, 96.6% of the stubble samples and 51.2% of the pasture samples. The classification results for the second application were not as high, with 68% for the low transformation samples, 58.8% for the medium transformation and 31.8% for the high transformation. Nonetheless, the confusion matrices indicated that the training samples were not enough, and more sampling should be provided for attaining better results. Given that GMMHMM is a sequential model, it also presented the temporal configuration of the acoustical patterns by their transition probabilities. This feature allowed us to emphasize the importance of conservation, when we found that the most stable and inaccessible states were associated to the most acoustically diverse ecosystems

Passive acoustic monitoring for assessment of natural and anthropogenic sound sources in the marine environment using automatic recognition

In the marine environment, sound can be an efficient source of information. Indeed, several marine species, including fish, use sound to navigate, select habitats, detect predators and prey, and to attract mates. Therefore, all the abiotic, biotic and manmade sounds that comprise the soundscape, have the potential to be used to assess and monitor species and marine environments. Passive acoustic monitoring (PAM) involves the use of acoustic sensors to record sound in the environment, from which relevant ecological information can be inferred. This thesis studied marine soundscapes, with special attention on fish communities, anthropogenic noise, and applied several methods to analyse acoustic recordings. Most of the focus was on the Tagus estuary, where the presence of two highly vocal species is known: the Lusitanian toadfish (Halobatrachus didactylus) and the meagre (Argyrosomus regius). Azorean and Mozambique soundscapes were also analysed. Several methods were applied to extract information and to visualize soundscape characteristics, including sound recognition systems based on hidden Markov models to recognize fish sounds and boat passages. Analysis of several types of marine environments and time scales showed several advantages and disadvantages of different methods. The use of sound pressure level on different frequency bands allowed the quantification of daily and seasonal patterns. Ecoacoustic indices appear to be cost-effective tools to monitor biodiversity in some marine environments. Using automatic recognition, vocal rhythms (diel and seasonal patterns) and vocal interactions among individuals were also characterized. Furthermore, boat noise effects on fish were studied: we encountered impacts on the audition, vocal behaviour and reproduction. Overall, we used PAM as a tool to remotely assess and monitor soundscapes, biodiversity, fish communities’ seasonal patterns, fish behaviour, species presence, and the effect of anthropogenic noise aiming to contribute for the management and conservation of marine ecosystems