Generalisation in environmental sound classification : the ‘making sense of sounds’ data set and challenge

Humans are able to identify a large number of environmental sounds and categorise them according to high-level semantic categories, e.g. urban sounds or music. They are also capable of generalising from past experience to new sounds when applying these categories. In this paper we report on the creation of a data set that is structured according to the top-level of a taxonomy derived from human judgements and the design of an associated machine learning challenge, in which strong generalisation abilities are required to be successful. We introduce a baseline classification system, a deep convolutional network, which showed strong performance with an average accuracy on the evaluation data of 80.8%. The result is discussed in the light of two alternative explanations: An unlikely accidental category bias in the sound recordings or a more plausible true acoustic grounding of the high-level categories

Clang, chitter, crunch : perceptual organisation of onomatopoeia

A method has been developed that utilizes a sound-sorting and labeling procedure, with correspondence analysis of participant-generated descriptive terms, to elicit perceptual categories of sound. Unlike many other methods for identifying perceptual categories, this approach allows for the interpretation of participant categorization without the researcher prescribing descriptive terms. The work has allowed robust sound taxonomies to be created, which give insight into categorical auditory processing of everyday sounds by humans. Work on common audio search terms has highlighted that onomatopoeia are an important group that has been largely overlooked in quotidian sound studies. These are words for which the meaning of the word maps onto the sound of the utterance, and are an example of sound symbolism where there is a non-arbitrary link between the form and the meaning of word. Early analysis of the data suggests that people do draw on sound symbolism to carry out the categorization, but that in addition they also draw similarities between the inferred sound sources, such as organic versus non-organic

Toward an evidence-based taxonomy of everyday sounds

An organizing account of everyday sounds could greatly simplify the management of audio data. The job of an audio database manager will typically involve assigning a combination of textual descriptors, and perhaps allocating to a predefined category. Retrieval is likely achieved by matching the descriptor to keyword search terms, or by browsing through categories. Whilst classification of musical instruments using this type of approach is relatively simple by virtue of the fact that a predefined taxonomy can follow a signal-related hierarchy, non-musical sounds do not necessarily follow such a hierarchy. In addition, classification is made more problematic by the ambiguity of words used to describe everyday sounds. Another area in which the issue of establishing a taxonomy of everyday sounds is particularly pertinent is that of soundscape research; research into soundscapes—acoustic environments as they are perceived—is a relatively new and multidisciplinary area, and as such descriptive terms for everyday sounds are currently used inconsistently. Many existing attempts to taxonomize everyday sounds prescribe categories that are not mutually exclusive, in that everyday sounds could exist in a number of categories; moreover, many are not based on empirical evidence. Here we present a robust method for creating an evidence-based taxonomy of everyday sounds, involving hierarchical clustering from dimensions generated by correspondence analysis of data from a simple card-sorting and naming procedur