Definition of the Opus Audio Codec

Vos, Koen; Terriberry, Timothy B.

doi:10.17487/rfc6716

Cited by 25 publications

(19 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The novel coronavirus disease (COVID- 19), declared a pandemic by the World Health Organization on March 11, 2020, has claimed over 950,000 lives worldwide as of September 2020 1 . Epidemiology experts concur that mass testing is essential for isolating infected individuals, contact tracing, and slowing the progression of the virus 2,3 .…”

Section: Background and Summarymentioning

confidence: 99%

“…One of the files contains the audio data, as it was directly received at the COUGHVID servers, and it can be in the WEBM 17 or OGG 18 formats, respectively with the .webm and .ogg extensions. In all cases, the audio codec is Opus 19 and the sampling frequency 48 kHz. The second file contains the metadata encoded as plain text in the JSON format 20 , and has the .json extension.…”

Section: Data Recordsmentioning

confidence: 99%

See 1 more Smart Citation

The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

Orlandic,

Teijeiro,

Atienza

2020

Preprint

View full text Add to dashboard Cite

Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. However, there is currently no validated database of cough sounds with which to train such ML models. The COUGHVID dataset provides over 20,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. First, we filtered the dataset using our open-sourced cough detection algorithm. Second, experienced pulmonologists labeled more than 2,000 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. Finally, we ensured that coughs labeled as symptomatic and COVID-19 originate from countries with high infection rates, and that their expert labels are consistent. As a result, the COUGHVID dataset contributes a wealth of cough recordings for training ML models to address the world's most urgent health crises.

show abstract

Section: Background and Summarymentioning

confidence: 99%

Section: Data Recordsmentioning

confidence: 99%

The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

Orlandic,

Teijeiro,

Atienza

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…One of the files contains the audio data, as it was directly received at the COUGHVID servers, and it can be in the WEBM 20 or OGG 21 formats, respectively with the .webm and .ogg extensions. In all cases, the audio codec is Opus 22 , with a sampling frequency of 48 kHz and operating in variable bitrate (VBR) mode. For more than 40% of the recordings, the effective bitrate is 48 kbit/s, which is in the highest bandwidth range for mono recordings 22 .…”

Section: Data Recordsmentioning

confidence: 99%

“…In all cases, the audio codec is Opus 22 , with a sampling frequency of 48 kHz and operating in variable bitrate (VBR) mode. For more than 40% of the recordings, the effective bitrate is 48 kbit/s, which is in the highest bandwidth range for mono recordings 22 . For the rest of the recordings, the reason for having a lower effective bitrate is the presence of long periods of silence, which is in fact desirable given the application.…”

Section: Data Recordsmentioning

confidence: 99%

The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms

2021

View full text Add to dashboard Cite

Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. The COUGHVID dataset provides over 25,000 crowdsourced cough recordings representing a wide range of participant ages, genders, geographic locations, and COVID-19 statuses. First, we contribute our open-sourced cough detection algorithm to the research community to assist in data robustness assessment. Second, four experienced physicians labeled more than 2,800 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. Finally, we ensured that coughs labeled as symptomatic and COVID-19 originate from countries with high infection rates. As a result, the COUGHVID dataset contributes a wealth of cough recordings for training ML models to address the world’s most urgent health crises.

show abstract

“…OPUS is an open-source lossy audio codec usable for both speech and music (Valin et al, 2012). It is nowadays used in many communication tools, as Zoom for video conferencing or WhatsApp for voice messages (Hazra and Mateti, 2017).…”

Section: Mpeg-1/mpeg-2 Audio Layer III (Mp3mentioning

confidence: 99%

A digital “flat affect”? Popular speech compression codecs and their effects on emotional prosody

Niebuhr

Siegert²

2023

Front. Commun.

View full text Add to dashboard Cite

IntroductionCalls via video apps, mobile phones and similar digital channels are a rapidly growing form of speech communication. Such calls are not only— and perhaps less and less— about exchanging content, but about creating, maintaining, and expanding social and business networks. In the phonetic code of speech, these social and emotional signals are considerably shaped by (or encoded in) prosody. However, according to previous studies, it is precisely this prosody that is significantly distorted by modern compression codecs. As a result, the identification of emotions becomes blurred and can even be lost to the extent that opposing emotions like joy and anger or disgust and sadness are no longer differentiated on the recipients' side. The present study searches for the acoustic origins of these perceptual findings.MethodA set of 108 sentences from the Berlin Database of Emotional Speech served as speech material in our study. The sentences were realized by professional actors (2m, 2f) with seven different emotions (neutral, fear, disgust, joy, boredom, anger, sadness) and acoustically analyzed in the original uncompressed (WAV) version and as well as in strongly compressed versions based on the four popular codecs AMR-WB, MP3, OPUS, and SPEEX. The analysis included 6 tonal (i.e. f0-related) and 7 non-tonal prosodic parameters (e.g., formants as well as acoustic-energy and spectral-slope estimates).ResultsResults show significant, codec-specific distortion effects on all 13 prosodic parameter measurements compared to the WAV reference condition. Means values of automatic measurement can, across sentences, deviate by up to 20% from the values of the WAV reference condition. Moreover, the effects go in opposite directions for tonal and non-tonal parameters. While tonal parameters are distorted by speech compression such that the acoustic differences between emotions are increased, compressing non-tonal parameters make the acoustic-prosodic profiles of emotions more similar to each other, particularly under MP3 and SPEEX compression.DiscussionThe term “flat affect” comes from the medical field and describes a person's inability to express or display emotions. So, does strong compression of emotional speech create a “digital flat affect”? The answer to this question is a conditional “yes”. We provided clear evidence for a “digital flat affect”. However, it seems less strongly pronounced in the present acoustic measurements than in previous perception data, and it manifests itself more strongly in non-tonal than in tonal parameters. We discuss the practical implications of our findings for the everyday use of digital communication devices and critically reflect on the generalizability of our findings, also with respect to their origins in the codecs' inner mechanics.

show abstract

Definition of the Opus Audio Codec

Cited by 25 publications

References 3 publications

The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms

A digital “flat affect”? Popular speech compression codecs and their effects on emotional prosody

Contact Info

Product

Resources

About