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Forest Sound Classification Dataset: FSC22

by Meelan Bandara, Roshinie Jayasundara, Isuru Ariyarathne, Dulani Meedeniya and Charith Perera

Sensors 2023, 23(4), 2032; https://doi.org/10.3390/s23042032 - 10 Feb 2023

Cited by 17 | Viewed by 7031

The study of environmental sound classification (ESC) has become popular over the years due to the intricate nature of environmental sounds and the evolution of deep learning (DL) techniques. Forest ESC is one use case of ESC, which has been widely experimented with recently to identify illegal activities inside a forest. However, at present, there is a limitation of public datasets specific to all the possible sounds in a forest environment. Most of the existing experiments have been done using generic environment sound datasets such as ESC-50, U8K, and FSD50K. Importantly, in DL-based sound classification, the lack of quality data can cause misguided information, and the predictions obtained remain questionable. Hence, there is a requirement for a well-defined benchmark forest environment sound dataset. This paper proposes FSC22, which fills the gap of a benchmark dataset for forest environmental sound classification. It includes 2025 sound clips under 27 acoustic classes, which contain possible sounds in a forest environment. We discuss the procedure of dataset preparation and validate it through different baseline sound classification models. Additionally, it provides an analysis of the new dataset compared to other available datasets. Therefore, this dataset can be used by researchers and developers who are working on forest observatory tasks. Full article

(This article belongs to the Section Internet of Things)

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18 pages, 674 KiB

Open AccessArticle

Modelling Timbral Hardness

by Andy Pearce, Tim Brookes and Russell Mason

Appl. Sci. 2019, 9(3), 466; https://doi.org/10.3390/app9030466 - 30 Jan 2019

Cited by 9 | Viewed by 3937

Abstract

Hardness is the most commonly searched timbral attribute within freesound.org, a commonly used online sound effect repository. A perceptual model of hardness was developed to enable the automatic generation of metadata to facilitate hardness-based filtering or sorting of search results. A training dataset was collected of 202 stimuli with 32 sound source types, and perceived hardness was assessed by a panel of listeners. A multilinear regression model was developed on six features: maximum bandwidth, attack centroid, midband level, percussive-to-harmonic ratio, onset strength, and log attack time. This model predicted the hardness of the training data with

R^{2}

= 0.76. It predicted hardness within a new dataset with

R^{2}

= 0.57, and predicted the rank order of individual sources perfectly, after accounting for the subjective variance of the ratings. Its performance exceeded that of human listeners. Full article

(This article belongs to the Special Issue Psychoacoustic Engineering and Applications)

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