Artificial ASMR: A Cyber-Psychological Approach
October 25, 2022 Β· Declared Dead Β· π International Workshop on Machine Learning for Signal Processing
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Zexin Fang, Bin Han, C. Clark Cao, Hans. D. Schotten
arXiv ID
2210.14321
Category
eess.AS: Audio & Speech
Cross-listed
cs.AI,
cs.MM,
cs.SD,
eess.SP
Citations
3
Venue
International Workshop on Machine Learning for Signal Processing
Last Checked
3 months ago
Abstract
The popularity of Autonomous Sensory Meridian Response (ASMR) has skyrockted over the past decade, but scientific studies on what exactly triggered ASMR effect remain few and immature, one most commonly acknowledged trigger is that ASMR clips typically provide rich semantic information. With our attention caught by the common acoustic patterns in ASMR audios, we investigate the correlation between the cyclic features of audio signals and their effectiveness in triggering ASMR effects. A cyber-psychological approach that combines signal processing, artificial intelligence, and experimental psychology is taken, with which we are able to quantize ASMR-related acoustic features, and therewith synthesize ASMR clips with random cyclic patterns but not delivering identifiably scenarios to the audience, which were proven to be effective in triggering ASMR effects.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Audio & Speech
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
LPCNet: Improving Neural Speech Synthesis Through Linear Prediction
R.I.P.
π»
Ghosted
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
R.I.P.
π»
Ghosted
TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech
R.I.P.
π»
Ghosted
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
R.I.P.
π»
Ghosted
Utterance-level Aggregation For Speaker Recognition In The Wild
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted