Use your trained ear to classify musical instruments in short audio clips and help improve AI-generated audio data. This flexible, two-day remote project pays a fixed $160.
About OpenTrain
OpenTrain AI is the hiring and contracting organization for this project. OpenTrain is the #1 platform for finding and building careers in AI training and data labeling, helping contributors discover projects, build a professional profile, and apply in minutes.
Creating an OpenTrain account is free. Your profile can help you show relevant experience, find opportunities aligned with your skills, and build a lasting portfolio in a growing technology field.
- Remote contractor opportunity
- Part-time project with flexible scheduling
- Apply and manage your work through OpenTrain
About AI Training and Audio Classification
AI training is the human side of building artificial intelligence. People prepare and review examples that help AI systems understand the world, including audio recordings, spoken language, music, and other sound data.
In this project, your musical expertise will help an AI system learn to identify instruments from short audio clips. Careful human classification and consistent feedback are essential for producing useful training data.
- Work directly on data used to improve AI systems
- Apply real-world musical listening expertise
- Contribute to a fast-growing field with flexible remote work
The Role
OpenTrain is seeking a Music Instrument Audio Evaluator for a short-term Music Instrument Audio Classification project. You will listen to short clips, identify the instrument family and specific instrument, and evaluate AI-generated audio data using your subject-matter knowledge.
Prior AI training experience is not required unless stated elsewhere on this job page. The key qualification is strong real-world knowledge of music and the ability to recognize instruments accurately by sound.
- Project length: Short-term, with a two-day work window
- Time requirement: 20+ hours per week
- Schedule: Flexible
- Pay: $160 fixed price
- Engagement: Contractor and part-time
What You'll Do
You will complete focused audio classification tasks using provided references and clear annotation guidelines. Consistency, accuracy, and careful listening will be important throughout the project.
- Listen to short audio clips of musical instruments
- Identify the instrument family, such as strings, brass, woodwind, percussion, or keyboard
- Identify specific instruments within each family
- Use provided audio references to guide classification decisions
- Follow annotation guidelines and deliver consistent, accurate work
- Evaluate and improve AI-generated audio data based on your expertise
Requirements
This role is listed at the entry level for AI training, but it requires established music or audio expertise. You should be comfortable making reliable judgments from sound and completing repetitive listening work independently.
- Bachelor’s degree in Music or a related field
- At least 3 years of experience in music performance, production, audio engineering, or similar work
- A trained ear and strong ability to recognize musical instruments by sound
- Strong English communication skills
- Comfort with focused, repetitive listening tasks
- Ability to work independently in a remote setting
Preferred Experience
Additional experience may help you work efficiently on this project, although it is not listed as required.
- Transcription experience
- Remixing experience
- Music information retrieval experience
- Previous AI training or data annotation experience
Project Details and Application
This is a flexible, short-term project with a two-day window and a fixed compensation of $160. Extension may be possible based on performance. Compensation is handled through the OpenTrain project budget fields shown on this job page.
Apply through OpenTrain to be considered for the Music Instrument Audio Evaluator project. If selected, you will use your listening expertise to help create more capable AI audio systems.
- Language: English
- Location: Worldwide
- Work arrangement: Remote
- Potential extension based on performance