RECOMMENDATION ITU-R BS.1534-1 - Method for the subjective assessment of intermediate quality level of coding systems

2003

Subfields: Evaluation & Benchmarks, Signal Processing

Citations in the corpus

• SoundStream: An End-to-End Neural Audio Codec (2021, methodology): “We have chosen a crowd-sourced methodology inspired by MUSHRA [53], with a hidden reference but no lowpass-filtered anchor.”
• LPCNET: Improving Neural Speech Synthesis through Linear Prediction (2018, methodology): “We conducted a subjective listening test with a MUSHRA-derived methodology [21], where 8 utterances (2 male and 2 female speakers) were each evaluated by 100 participants.”
• Speaker Adaptation of a Multilingual Acoustic Model for Cross-Language Synthesis (2020, unknown): “The samples were presented to native speakers in a MUSHRA-like questionnaire [30].”
• webMUSHRA — A Comprehensive Framework for Web-based Listening Tests (2018, methodology): “The listeners are asked to assess the impairments on “B” compared to “A”, and “C” compared to “A”, according to the continuous quality scale (CQS) [2].”
• TTS voice corpus reduction for audio-book generation (2020, methodology): “Samples from 6 voice corpus sizes and two corpus reduction methods are used to design a MUSHRA test (Recommendation, 2003).”