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Abstract Acoustic recordings of soundscapes are an important category of audio data that can be useful for answering a variety of questions, and an entire discipline within ecology, dubbed “soundscape ecology,” has risen to study them. Bird sound is often the focus of studies of soundscapes due to the ubiquitousness of birds in most terrestrial environments and their high vocal activity. Autonomous acoustic recorders have increased the quantity and availability of recordings of natural soundscapes while mitigating the impact of human observers on community behavior. However, such recordings are of little use without analysis of the sounds they contain. Manual analysis currently stands as the best means of processing this form of data for use in certain applications within soundscape ecology, but it is a laborious task, sometimes requiring many hours of human review to process comparatively few hours of recording. For this reason, few annotated data sets of soundscape recordings are publicly available. Further still, there are no publicly available strongly labeled soundscape recordings of bird sounds that contain information on timing, frequency, and species. Therefore, we present the first data set of strongly labeled bird sound soundscape recordings under free use license. These data were collected in the Northeastern United States at Powdermill Nature Reserve, Rector, Pennsylvania, USA. Recordings encompass 385 minutes of dawn chorus recordings collected by autonomous acoustic recorders between the months of April through July 2018. Recordings were collected in continuous bouts on four days during the study period and contain 48 species and 16,052 annotations. Applications of this data set may be numerous and include the training, validation, and testing of certain advanced machine‐learning models that detect or classify bird sounds. There are no copyright or propriety restrictions; please cite this paper when using materials within.
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Scaper: A library for soundscape synthesis and augmentation
Sound event detection (SED) in environmental recordings is a key topic of research in machine listening, with applications in noise monitoring for smart cities, self-driving cars, surveillance, bioa-coustic monitoring, and indexing of large multimedia collections. Developing new solutions for SED often relies on the availability of strongly labeled audio recordings, where the annotation includes the onset, offset and source of every event. Generating such precise annotations manually is very time consuming, and as a result existing datasets for SED with strong labels are scarce and limited in size. To address this issue, we present Scaper, an open-source library for soundscape synthesis and augmentation. Given a collection of iso-lated sound events, Scaper acts as a high-level sequencer that can generate multiple soundscapes from a single, probabilistically defined, 'specification'. To increase the variability of the output, Scaper supports the application of audio transformations such as pitch shifting and time stretching individually to every event. To illustrate the potential of the library, we generate a dataset of 10,000 sound-scapes and use it to compare the performance of two state-of-The-Art algorithms, including a breakdown by soundscape characteristics. We also describe how Scaper was used to generate audio stimuli for an audio labeling crowdsourcing experiment, and conclude with a discussion of Scaper's limitations and potential applications.
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- Award ID(s):
- 1633259
- PAR ID:
- 10074705
- Date Published:
- Journal Name:
- Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA-17)
- Page Range / eLocation ID:
- 344-348
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WASPAA.2017.8170052
