Audio Annotation

Audio Classification Types

Classification of Acoustic Data

This type of data annotation entails pinpointing the precise location where the sounds were captured. Differentiating between various contexts, including houses, schools, cafés, and nearly anything else, is necessary for data annotators. This is highly helpful for establishing monitoring systems as well as sound libraries for audio multimedia.

Classification of environmental sound

The data annotators must, as the name suggests, classify diverse noises that might be ascribed to distinct settings. For instance, there are some noises that are unique to cities, such as sirens, automobile horns, and construction noise. This is highly helpful for both predictive maintenance and the development of security systems that can recognize the noises of break-ins.

Classification of Music

Numerous elements might be categorized in this case, including the genre, the instruments used, the type of ensemble, and many more. This kind of annotation is excellent for streamlining user suggestions and organizing music collections.

Classification of Natural Language Utterances

This kind of annotation necessitates categorizing minute features like dialect, semantics, and many other components of human speech. This is crucial because it enables chatbots and other virtual support to comprehend human speech more effectively.

Industries need Audio Annotation Services:

Automative

When we must classify all the background sounds occurring inside the car, including the radio, laughing, yelling, singing, animals, and even quiet, we are completing a complex audio annotation assignment. Additionally, some noises had to be classed according to the degree of aggression and the environment they produce (positive, negative, neutral). Up to 8 audio tracks required to have one set of sounds indicated on each track as part of the project.

• Technique: Tracking events
• 2 500 audio files total
• 15 days for completion
• A 45 FTE team
• superior to 99 percent

 Entertainment and the Media

We had to categorize and list every sound that was audible in the video for this assignment. Most of the noises were from musical instruments, and it was exceedingly challenging to tell out the sounds from other instruments that belonged to the same group (for example, plucked strings). There were also noises of the outdoors, animals, human conversation, and emotions on the soundtrack. In total, there were more than 750 labels.

• Applied Method: Sound Labeling
• 100 000 audio files in size
• Time to completion: 18 days
• 10-person team
• Quality: above 95%

Security

It was our responsibility to categorize each audio file in accordance with the noises captured on the recording. Voices (female, male, kid), emotions (crying, yelling, laughing), sounds of nature (rain, wind, thunder), and city noises were to be distinguished (car horn, traffic noise). In total, there were more than 50 labels.

• Applied Method: Audio Classification
• 80 000 audio files total
• Time to completion: 10 days
• Team: 6 FTE
• Quality: above 95%

Information Technologies

As part of the project, it was important to convert audio recordings of speech into text while maintaining a high standard of literacy and proper punctuation. German, French, Italian, and English were the four languages used for the audio recordings. The annotators’ job was made more difficult by the audio, which was a recording of individuals speaking in various accents.

• Speech-to-text conversion method; 800 hours of audio
• 60 days for completion
• Team: 21 FTE
• superior to 98 percent