Audio Annotation Services

Audio annotation plays an important role in the development of chatbots, virtual assistants and other NLP technology. Mindy Support provides comprehensive audio annotation services covering all of the various annotation types listed below.

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Types of Audio Annotation Services We Provide

Sound Labeling

With sound labeling, the data annotators are given a recording and they need to separate all of the needed sounds and label them. For example, these can be certain keywords or the sound of a specific musical instrument.

Event Tracking

Event tracking evaluates performance of the sound event detection systems in multisource conditions similar to our everyday life, where the sound sources are rarely heard in isolation. In this task, there is no control over the number of overlapping sound events at each time, not in the training nor in the testing audio data.

Speech to Text Transcription

Speech to text transcription is an important part of creating NLP technology. It involves taking recorded speech and transcribing them to text while carefully labeling both words and sounds that the person pronounces. It is also important to use the right punctuation as well.

Audio Classification

Audio classification is listening and analyzing audio recordings. Using this data, the machines are able to differentiate between sounds and voice commands. This type of audio annotation is important in the development of virtual assistants, automatic speech recognition and text to speech systems. There are many different types of audio classification:

Types of Audio Classification

  • Acoustic Data Classification
  • Environmental Sound Classification
  • Music Classification
  • Natural Language Utterance Classification

    Acoustic Data Classification

  • This form of data annotation involves identifying exactly where the sounds were recorded. The data annotators need to differentiate between all kinds of environments such as homes, schools, cafes and almost anything else. This is very useful for maintaining sound libraries for audio multimedia and in creating monitoring systems as well.
  • Environmental Sound Classification

  • Just like the name implies, the data annotators need to categorize various sounds that can be attributed to various environments. For example, there are certain sounds that are specific to cities such as construction, car horns, sirens and many other sounds. This is very useful for creating security systems that can identify sounds of break-ins and also for predictive maintenance as well.
  • Music Classification

  • There are many things that could be classified here such as the genre, instruments played, ensemble type and many other things as well. This type of annotation is very useful for organizing music libraries and improving user recommendations.
  • Natural Language Utterance Classification

  • This type of annotation requires classifying small details such as dialect, semantics and many other things found in human speech. This is very important because this is what allows chatbots and virtual assistance to better understand human speech.

    Acoustic Data Classification

  • This form of data annotation involves identifying exactly where the sounds were recorded. The data annotators need to differentiate between all kinds of environments such as homes, schools, cafes and almost anything else. This is very useful for maintaining sound libraries for audio multimedia and in creating monitoring systems as well.
  • Environmental Sound Classification

  • Just like the name implies, the data annotators need to categorize various sounds that can be attributed to various environments. For example, there are certain sounds that are specific to cities such as construction, car horns, sirens and many other sounds. This is very useful for creating security systems that can identify sounds of break-ins and also for predictive maintenance as well.
  • Music Classification

  • There are many things that could be classified here such as the genre, instruments played, ensemble type and many other things as well. This type of annotation is very useful for organizing music libraries and improving user recommendations.
  • Natural Language Utterance Classification

  • This type of annotation requires classifying small details such as dialect, semantics and many other things found in human speech. This is very important because this is what allows chatbots and virtual assistance to better understand human speech.

Multi Label Audio Annotation Tasks

Multi label audio annotation is the process of placing multiple labels to identify overlapping sound sources in temporally-complex urban soundscapes. There are three main types of tasks:

Binary-labeling

Determine whether a single suggested sound-source class was present or not in the recording. This task type provided both positive and negative labels explicitly.

One-stage multi-labeling

The data annotators are presented with a list of class labels and they need to select all the sound-source classes present in the audio.

Two-stage hierarchical multi-labeling

In the first stage, the audio is presented to the data annotators alongside a list of superclass labels and they need to identify the sounds. At the second stage, this same audio is given to a different data annotator This task type provides positive labels explicitly and negative labels implicitly.

Our Clients

Helping Doctors and Researchers Facilitate Immunology Research

Our data annotators needed to track the movements of various cells and account for the trajectors of each cell. It was very important that everything was done right since this would be later used for immunology research.

Results Delivered to the Client

  • 98% Quality Rate
  • 5-6 Videos were annotated every week
  • We assembled an entire team in 4 days and annotated 10,000+ images

Video Annotation for IT & Computer Software

Managers and stakeholders need to be able to monitor the pace of construction and make sure safety precautions are being followed. We helped the client lower costs and keep the project on schedule by annotating more than 300 hours of video.

Results Delivered to the Client

  • 98% Quality Rate
  • 5-6 Videos were annotated every week
  • We assembled an entire team in 4 days and annotated 10,000+ images

Action Tracking People’s Movements in Videos Using Labeling

The client is working on a very large project which required tracking people’s movements in videos. This included things like running, walking, bike-riding and many other things. We needed to label the sequence of events when all of these movements were taking place.

Results Delivered to the Client

  • 15 action classes annotated
  • >95% accuracy
  • 30 full-time data annotators working on project

Video Annotation for Sports

We are working with a company that provides software for the analysis of sporting matches. We had to specify the timestamps of the events, the team name, event, comment, and other specific attributes.

Results Delivered to the Client

  • 98% Quality Rate
  • 5-6 Videos were annotated every week
  • We assembled an entire team in 4 days and annotated 10,000+ images

Tagging the Make and Models of Cars in Videos

The client was working on a product that would use computer vision to distinguish between all of the various makes and models of cars. We needed to annotate a dataset of 50,000 short videos that were taken in various parking lots.

Results Delivered to the Client

  • 50,000 videos annotated
  • Classified cars into 750 classes
  • Everything was done in 15 days
  • >95% quality score

Video Annotation for Human Emotions

We needed to classify the emotional coloring for the whole video by the dialogs in it. Annotate time periods on video with different sounds. Choose the best corresponding label for the type of sound. We hired data annotators with knowledge of German to do the work.

Results Delivered to the Client

  • 98% Quality Rate
  • 5-6 Videos were annotated every week
  • We assembled an entire team in 4 days and annotated 10,000+ images

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