| 1. |
- Eyben, Florian, et al.
(författare)
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Emotion in the singing voice—a deeper look at acoustic features in the light of automatic classification
- 2015
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Ingår i: EURASIP Journal on Audio, Speech, and Music Processing. - : Springer Science and Business Media LLC. - 1687-4714 .- 1687-4722.
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Tidskriftsartikel (refereegranskat)abstract
- We investigate the automatic recognition of emotions in the singing voice and study the worth and role of a variety of relevant acoustic parameters. The data set contains phrases and vocalises sung by eight renowned professional opera singers in ten different emotions and a neutral state. The states are mapped to ternary arousal and valence labels. We propose a small set of relevant acoustic features basing on our previous findings on the same data and compare it with a large-scale state-of-the-art feature set for paralinguistics recognition, the baseline feature set of the Interspeech 2013 Computational Paralinguistics ChallengE (ComParE). A feature importance analysis with respect to classification accuracy and correlation of features with the targets is provided in the paper. Results show that the classification performance with both feature sets is similar for arousal, while the ComParE set is superior for valence. Intra singer feature ranking criteria further improve the classification accuracy in a leave-one-singer-out cross validation significantly.
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| 2. |
- Landsiedel, Christian, et al.
(författare)
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Syllabification of conversational speech using bidirectional long-short-term memory neural networks
- 2011
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Ingår i: Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on. - Prague, Czech Republic. ; , s. 5256-5259
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Konferensbidrag (refereegranskat)abstract
- Segmentation of speech signals is a crucial task in many types of speech analysis. We present a novel approach at segmentation on a syllable level, using a Bidirectional Long-Short-Term Memory Neural Network. It performs estimation of syllable nucleus positions based on regression of perceptually motivated input features to a smooth target function. Peak selection is performed to attain valid nuclei positions. Performance of the model is evaluated on the levels of both syllables and the vowel segments making up the syllable nuclei. The general applicability of the approach is illustrated by good results for two common databases - Switchboard and TIMIT - for both read and spontaneous speech, and a favourable comparison with other published results.
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| 3. |
- Schuller, Björn W., et al.
(författare)
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Towards Sonification in Multimodal and User-Friendly Explainable Artificial Intelligence
- 2021
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Ingår i: ICMI '21. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450384810 ; , s. 788-792
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Konferensbidrag (refereegranskat)abstract
- We are largely used to hearing explanations. For example, if someone thinks you are sad today, they might reply to your “why?” with “because you were so Hmmmmm-mmm-mmm”. Today’s Artificial Intelligence (AI), however, is – if at all – largely providing explanations of decisions in a visual or textual manner. While such approaches are good for communication via visual media such as in research papers or screens of intelligent devices, they may not always be the best way to explain; especially when the end user is not an expert. In particular, when the AI’s task is about Audio Intelligence, visual explanations appear less intuitive than audible, sonified ones. Sonification has also great potential for explainable AI (XAI) in systems that deal with non-audio data – for example, because it does not require visual contact or active attention of a user. Hence, sonified explanations of AI decisions face a challenging, yet highly promising and pioneering task. That involves incorporating innovative XAI algorithms to allow pointing back at the learning data responsible for decisions made by an AI, and to include decomposition of the data to identify salient aspects. It further aims to identify the components of the preprocessing, feature representation, and learnt attention patterns that are responsible for the decisions. Finally, it targets decision-making at the model-level, to provide a holistic explanation of the chain of processing in typical pattern recognition problems from end-to-end. Sonified AI explanations will need to unite methods for sonification of the identified aspects that benefit decisions, decomposition and recomposition of audio to sonify which parts in the audio were responsible for the decision, and rendering attention patterns and salient feature representations audible. Benchmarking sonified XAI is challenging, as it will require a comparison against a backdrop of existing, state-of-the-art visual and textual alternatives, as well as synergistic complementation of all modalities in user evaluations. Sonified AI explanations will need to target different user groups to allow personalisation of the sonification experience for different user needs, to lead to a major breakthrough in comprehensibility of AI via hearing how decisions are made, hence supporting tomorrow’s humane AI’s trustability. Here, we introduce and motivate the general idea, and provide accompanying considerations including milestones of realisation of sonifed XAI and foreseeable risks.
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| 4. |
- Seiferth, Caroline, et al.
(författare)
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How to e-mental health : a guideline for researchers and practitioners using digital technology in the context of mental health
- 2023
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Ingår i: Nature Mental Health. - : Springer Nature. - 2731-6076. ; 1:8, s. 542-554
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Tidskriftsartikel (refereegranskat)abstract
- Despite an exponentially growing number of digital or e-mental health services, methodological guidelines for research and practical implementation are scarce. Here we aim to promote the methodological quality, evidence and long-term implementation of technical innovations in the healthcare system. This expert consensus is based on an iterative Delphi adapted process and provides an overview of the current state-of-the-art guidelines and practical recommendations on the most relevant topics in e-mental health assessment and intervention. Covering three objectives, that is, development, study specifics and intervention evaluation, 11 topics were addressed and co-reviewed by 25 international experts and a think tank in the field of e-mental health. This expert consensus provides a comprehensive essence of scientific knowledge and practical recommendations for e-mental health researchers and clinicians. This way, we aim to enhance the promise of e-mental health: low-threshold access to mental health treatment worldwide.
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