A soundscape assessment of the Sasso Fratino Integral Nature Reserve in the Central Apennines, Italy

Righini, Roberta; Pavan, Gianni

doi:10.1080/14888386.2019.1696229

Cited by 16 publications

(9 citation statements)

References 42 publications

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“…We measured the performance of the DNNs in detecting Bulbul’s vocalizations using a test dataset of 3 h with several thousand individual calls, which also contained high background noise from other birds and animals, as well as anthropophonic and geophonic sounds ( Righini and Pavan, 2020 ). The test dataset was pre-processed with the same procedure used for the training dataset, which included MFB calculation, median clipping, and small object removal.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, in many studies the manual analysis is based on a limited amount of data and may miss out patterns which may be revealed only if enough data is automatically processed and analyzed. In a broader scope, the development of advanced automated tools for bio-acoustic analysis can support large-scale research and reveal organisms’ vocal communication patterns, may facilitate monitoring of populations, and can be leveraged for management and conservation efforts in natural environments ( Righini and Pavan, 2020 ; Kahl et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Identification, Analysis and Characterization of Base Units of Bird Vocal Communication: The White Spectacled Bulbul (Pycnonotus xanthopygos) as a Case Study

Marck

Vortman

Kolodny

et al. 2022

Front. Behav. Neurosci.

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Animal vocal communication is a broad and multi-disciplinary field of research. Studying various aspects of communication can provide key elements for understanding animal behavior, evolution, and cognition. Given the large amount of acoustic data accumulated from automated recorders, for which manual annotation and analysis is impractical, there is a growing need to develop algorithms and automatic methods for analyzing and identifying animal sounds. In this study we developed an automatic detection and analysis system based on audio signal processing algorithms and deep learning that is capable of processing and analyzing large volumes of data without human bias. We selected the White Spectacled Bulbul (Pycnonotus xanthopygos) as our bird model because it has a complex vocal communication system with a large repertoire which is used by both sexes, year-round. It is a common, widespread passerine in Israel, which is relatively easy to locate and record in a broad range of habitats. Like many passerines, the Bulbul’s vocal communication consists of two primary hierarchies of utterances, syllables and words. To extract each of these units’ characteristics, the fundamental frequency contour was modeled using a low degree Legendre polynomial, enabling it to capture the different patterns of variation from different vocalizations, so that each pattern could be effectively expressed using very few coefficients. In addition, a mel-spectrogram was computed for each unit, and several features were extracted both in the time-domain (e.g., zero-crossing rate and energy) and frequency-domain (e.g., spectral centroid and spectral flatness). We applied both linear and non-linear dimensionality reduction algorithms on feature vectors and validated the findings that were obtained manually, namely by listening and examining the spectrograms visually. Using these algorithms, we show that the Bulbul has a complex vocabulary of more than 30 words, that there are multiple syllables that are combined in different words, and that a particular syllable can appear in several words. Using our system, researchers will be able to analyze hundreds of hours of audio recordings, to obtain objective evaluation of repertoires, and to identify different vocal units and distinguish between them, thus gaining a broad perspective on bird vocal communication.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification, Analysis and Characterization of Base Units of Bird Vocal Communication: The White Spectacled Bulbul (Pycnonotus xanthopygos) as a Case Study

Marck

Vortman

Kolodny

et al. 2022

Front. Behav. Neurosci.

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“…Break-out spectrograms show specific signals on a finer temporal scale (Erbe et al 2016b). Alternatively, long-term spectrograms may display minimum (LTSmin), maximum (LTSmax), median (LTSmed), or other percentile levels (e.g., LTS75), computed within each frequency bin over some time window (Righini and Pavan 2020). The minima will track the quietest baseline and the maxima can highlight strong but brief Fig.…”

Section: 53mentioning

confidence: 99%

“…How many undisturbed soundscapes remain in this world of decreasing biodiversity, changes in land-use, and rising anthropogenic noise? Can the soundscape of a pristine habitat function as a model to restore a degraded habitat (Pavan 2017;Gordon et al 2019;Righini and Pavan 2020)? This chapter gives an overview of terrestrial and aquatic soundscapes, outlines how soundscapes may change or have changed over time, provides tools for analyzing and quantifying soundscapes, and discusses how passive acoustic monitoring applies to soundscape ecology research, management, and conservation.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Soundscapes as an Ecological Tool

Schoeman

Erbe

Pavan

et al. 2022

Exploring Animal Behavior Through Sound: Volume 1

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Soundscapes have been likened to acoustic landscapes, encompassing all the acoustic features of an area. The sounds that make up a soundscape can be grouped according to their source into biophony (sounds from animals), geophony (sounds from atmospheric and geophysical events), and anthropophony (sounds from human activities). Natural soundscapes have changed over time because of human activities that generate sound, alter land-use patterns, remove animals from natural settings, and result in climate change. These human activities have direct and indirect effects on animal distribution patterns and (acoustic) behavior. Consequently, current soundscapes may be very different from those a few hundred years ago. This is of concern as natural soundscapes have ecological value. Losing natural soundscapes may, therefore, result in a loss of biodiversity and ecosystem functioning. The study of soundscapes can identify ecosystems undergoing change and potentially document causes (such as noise from human activities). Methods for studying soundscapes range from listening and creating visual (spectrographic) displays to the computation of acoustic indices and advanced statistical modeling. Passive acoustic recording has become an ecological tool for research, monitoring, and ultimately conservation management. This chapter introduces terrestrial and aquatic soundscapes, soundscape analysis tools, and soundscape management.

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“…A critical challenge in PAM studies is the analysis and handling of very large amounts of acoustic data, especially for programs spanning wide temporal or spatial extents [19]. However, manual analysis is still the primary method for extracting biological information from PAM recordings [20], which typically combines aural and visual inspection of spectrograms [21,22] to achieve graphical representations of acoustic events connected to biophony, geophony, and anthrophony, as well as a general overview of the daily acoustic pattern [21]. When experienced observers are involved, manual analysis is always considered to be the most accurate, but it is time-consuming, costly, frequently subjective, and ultimately fails to be applied across broad spatiotemporal scales [22].…”

Section: Introductionmentioning

confidence: 99%

Diversity Monitoring of Coexisting Birds in Urban Forests by Integrating Spectrograms and Object-Based Image Analysis

Zhao

Yan

Jin

et al. 2022

Forests

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In the context of rapid urbanization, urban foresters are actively seeking management monitoring programs that address the challenges of urban biodiversity loss. Passive acoustic monitoring (PAM) has attracted attention because it allows for the collection of data passively, objectively, and continuously across large areas and for extended periods. However, it continues to be a difficult subject due to the massive amount of information that audio recordings contain. Most existing automated analysis methods have limitations in their application in urban areas, with unclear ecological relevance and efficacy. To better support urban forest biodiversity monitoring, we present a novel methodology for automatically extracting bird vocalizations from spectrograms of field audio recordings, integrating object-based classification. We applied this approach to acoustic data from an urban forest in Beijing and achieved an accuracy of 93.55% (±4.78%) in vocalization recognition while requiring less than ⅛ of the time needed for traditional inspection. The difference in efficiency would become more significant as the data size increases because object-based classification allows for batch processing of spectrograms. Using the extracted vocalizations, a series of acoustic and morphological features of bird-vocalization syllables (syllable feature metrics, SFMs) could be calculated to better quantify acoustic events and describe the soundscape. A significant correlation between the SFMs and biodiversity indices was found, with 57% of the variance in species richness, 41% in Shannon’s diversity index and 38% in Simpson’s diversity index being explained by SFMs. Therefore, our proposed method provides an effective complementary tool to existing automated methods for long-term urban forest biodiversity monitoring and conservation.

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A soundscape assessment of the Sasso Fratino Integral Nature Reserve in the Central Apennines, Italy

Cited by 16 publications

References 42 publications

Identification, Analysis and Characterization of Base Units of Bird Vocal Communication: The White Spectacled Bulbul (Pycnonotus xanthopygos) as a Case Study

Identification, Analysis and Characterization of Base Units of Bird Vocal Communication: The White Spectacled Bulbul (Pycnonotus xanthopygos) as a Case Study

Analysis of Soundscapes as an Ecological Tool

Diversity Monitoring of Coexisting Birds in Urban Forests by Integrating Spectrograms and Object-Based Image Analysis

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