The absence of the queen in a beehive is a very strong indicator of the need for beekeeper intervention. Manually searching for the queen is an arduous recurrent task for beekeepers that disrupts the normal life cycle of the beehive and can be a source of stress for bees. Sound is an indicator for signalling different states of the beehive, including the absence of the queen bee. In this work, we apply machine learning methods to automatically recognise different states in a beehive using audio as input. We investigate both support vector machines and convolutional neural networks for beehive state recognition, using audio data of beehives collected from the NU-Hive project. Results indicate the potential of machine learning methods as well as the challenges of generalizing the system to new hives.
Acoustic Scene Classification (ASC) and Sound Event Detection (SED) are two separate tasks in the field of computational sound scene analysis. In this work, we present a new dataset with both sound scene and sound event labels and use this to demonstrate a novel method for jointly classifying sound scenes and recognizing sound events. We show that by taking a joint approach, learning is more efficient and whilst improvements are still needed for sound event detection, SED results are robust in a dataset where the sample distribution is skewed towards sound scenes.
To undertake machine lip-reading, we try to recognise speech from a visual signal. Current work often uses viseme classification supported by language models with varying degrees of success. A few recent works suggest phoneme classification, in the right circumstances, can outperform viseme classification. In this work we present a novel two-pass method of training phoneme classifiers which uses previously trained visemes in the first pass. With our new training algorithm, we show classification performance which significantly improves on previous lip-reading results.
Visemes are the visual equivalent of phonemes. Although not precisely defined, a working definition of a viseme is “a set of phonemes which have identical appearance on the lips”. Therefore a phoneme falls into one viseme class but a viseme may represent many phonemes: a many to one mapping. This mapping introduces ambiguity between phonemes when using viseme classifiers. Not only is this ambiguity damaging to the performance of audio-visual classifiers operating on real expressive speech, there is also considerable choice between possible mappings. In this paper we explore the issue of this choice of viseme-to-phoneme map. We show that there is definite difference in performance between viseme-to-phoneme mappings and explore why some maps appear to work better than others. We also devise a new algorithm for constructing phoneme-to-viseme mappings from labeled speech data. These new visemes, ‘Bear’ visemes, are shown to perform better than previously known units
A critical assumption of all current visual speech recognition systems is that there are visual speech units called visemes which can be mapped to units of acoustic speech, the phonemes. Despite there being a number of published maps it is infrequent to see the effectiveness of these tested, particularly on visual-only lip-reading (many works use audio-visual speech). Here we examine 120 mappings and consider if any are stable across talkers. We show a method for devising maps based on phoneme confusions from an automated lip-reading system, and we present new mappings that show improvements for individual talkers.
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