59 citations · 115 across the 19 of their papers we have counts for
24 papers
An evaluation of data augmentation methods for sound scene geotagging
Helen L. Bear, Veronica Morfi, Emmanouil Benetos
Sound scene geotagging is a new topic of research which has evolved from acoustic scene classification. It is motivated by the idea of audio surveillance. Not content with only des…
Visually Exploring Multi-Purpose Audio Data
David Heise, Helen L. Bear
We analyse multi-purpose audio using tools to visualise similarities within the data that may be observed via unsupervised methods. The success of machine learning classifiers is a…
Memory Controlled Sequential Self Attention for Sound Recognition
Arjun Pankajakshan, Helen L. Bear, Vinod Subramanian +1
In this paper we investigate the importance of the extent of memory in sequential self attention for sound recognition. We propose to use a memory controlled sequential self attent…
Alternative Visual Units for an Optimized Phoneme-Based Lipreading System
Helen Bear, Richard Harvey
Lipreading is understanding speech from observed lip movements. An observed series of lip motions is an ordered sequence of visual lip gestures. These gestures are commonly known,…
Polyphonic Sound Event and Sound Activity Detection: A Multi-task approach
Arjun Pankajakshan, Helen L. Bear, Emmanouil Benetos
Polyphonic Sound Event Detection (SED) in real-world recordings is a challenging task because of the dynamic polyphony level, intensity, and duration of sound events. Current polyp…
City classification from multiple real-world sound scenes
Helen L. Bear, Toni Heittola, Annamaria Mesaros +2
The majority of sound scene analysis work focuses on one of two clearly defined tasks: acoustic scene classification or sound event detection. Whilst this separation of tasks is us…
Towards joint sound scene and polyphonic sound event recognition
Helen L. Bear, Ines Nolasco, Emmanouil Benetos
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 datase…
Audio-based identification of beehive states
Inês Nolasco, Alessandro Terenzi, Stefania Cecchi +3
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 beekeeper…
The speaker-independent lipreading play-off; a survey of lipreading machines
Jake Burton, David Frank, Madhi Saleh +2
Lipreading is a difficult gesture classification task. One problem in computer lipreading is speaker-independence. Speaker-independence means to achieve the same accuracy on test s…
An extensible cluster-graph taxonomy for open set sound scene analysis
Helen L Bear, Emmanouil Benetos
We present a new extensible and divisible taxonomy for open set sound scene analysis. This new model allows complex scene analysis with tangible descriptors and perception labels.…
Visual Speech Language Models
Helen L Bear
Language models (LM) are very powerful in lipreading systems. Language models built upon the ground truth utterances of datasets learn grammar and structure rules of words and sent…
Comparing heterogeneous visual gestures for measuring the diversity of visual speech signals
Helen L Bear, Richard Harvey
Visual lip gestures observed whilst lipreading have a few working definitions, the most common two are; `the visual equivalent of a phoneme' and `phonemes which are indistinguishab…
Phoneme-to-viseme mappings: the good, the bad, and the ugly
Helen L Bear, Richard Harvey
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".…
Comparing phonemes and visemes with DNN-based lipreading
Kwanchiva Thangthai, Helen L Bear, Richard Harvey
There is debate if phoneme or viseme units are the most effective for a lipreading system. Some studies use phoneme units even though phonemes describe unique short sounds; other s…
Understanding the visual speech signal
Helen L Bear
For machines to lipread, or understand speech from lip movement, they decode lip-motions (known as visemes) into the spoken sounds. We investigate the visual speech channel to furt…
Visual gesture variability between talkers in continuous visual speech
Helen L Bear
Recent adoption of deep learning methods to the field of machine lipreading research gives us two options to pursue to improve system performance. Either, we develop end-to-end sys…
Visual speech recognition: aligning terminologies for better understanding
Helen L Bear, Sarah Taylor
We are at an exciting time for machine lipreading. Traditional research stemmed from the adaptation of audio recognition systems. But now, the computer vision community is also par…
Decoding visemes: improving machine lipreading
Helen L Bear
Machine lipreading (MLR) is speech recognition from visual cues and a niche research problem in speech processing & computer vision. Current challenges fall into two groups: the co…
Decoding visemes: improving machine lipreading
Helen L. Bear, Richard Harvey
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 o…
Finding phonemes: improving machine lip-reading
Helen L. Bear, Richard W. Harvey, Yuxuan Lan
In machine lip-reading there is continued debate and research around the correct classes to be used for recognition. In this paper we use a structured approach for devising speaker…
Speaker-independent machine lip-reading with speaker-dependent viseme classifiers
Helen L. Bear, Stephen J. Cox, Richard W. Harvey
In machine lip-reading, which is identification of speech from visual-only information, there is evidence to show that visual speech is highly dependent upon the speaker [1]. Here,…
Which phoneme-to-viseme maps best improve visual-only computer lip-reading?
Helen L. Bear, Richard W. Harvey, Barry-John Theobald +1
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 pho…
Some observations on computer lip-reading: moving from the dream to the reality
Helen L. Bear, Gari Owen, Richard Harvey +1
In the quest for greater computer lip-reading performance there are a number of tacit assumptions which are either present in the datasets (high resolution for example) or in the m…
Resolution limits on visual speech recognition
Helen L. Bear, Richard Harvey, Barry-John Theobald +1
Visual-only speech recognition is dependent upon a number of factors that can be difficult to control, such as: lighting; identity; motion; emotion and expression. But some factors…