Labeled data for audio search models is typically acquired through a combination of manual annotation, synthetic data generation, and leveraging existing datasets. Each method addresses specific needs, such as ensuring accuracy, scaling data volume, or adapting to niche use cases. The choice depends on factors like budget, domain specificity, and the availability of pre-labeled resources.
Manual annotation involves humans listening to audio clips and assigning labels. For example, if building a model to detect emergency sirens, annotators might listen to urban noise recordings and tag segments containing sirens. Platforms like Amazon Mechanical Turk or specialized labeling services (e.g., Rev, Appen) are often used for this. However, this approach is time-consuming and costly, especially for large datasets. To improve efficiency, teams might use tools like Audacity or Praat to visualize waveforms and spectrograms, making it easier for annotators to pinpoint relevant sections. For specialized domains, such as medical audio (e.g., lung sounds), domain experts like doctors may be required to ensure accurate labeling.
Synthetic data generation creates labeled audio by programmatically mixing or modifying existing sounds. For instance, to train a model to recognize overlapping voices, you could overlay speech samples from a clean dataset like LibriSpeech with background noise from ESC-50. Tools like Audiomentations or custom scripts can apply effects like pitch shifting or reverb to simulate real-world conditions. This method is scalable and ensures precise labels since the synthetic combinations are known. However, synthetic data may lack the complexity of real-world audio, requiring validation against genuine recordings. A practical example is generating “wake word” training data by mixing “Hey Alexa” utterances with varying room acoustics.
Existing public or licensed datasets provide a cost-effective starting point. Datasets like AudioSet (YouTube clips labeled with 527 sound classes) or CommonVoice (crowdsourced speech) offer pre-labeled audio for general-purpose models. For domain-specific tasks, niche datasets like BirdVox (bird calls) or UrbanSound (urban noises) can be used. APIs like Spotify’s or YouTube’s can also serve as sources if licensing permits. Developers often fine-tune models trained on these datasets with smaller custom-labeled sets. For example, a music search app might start with AudioSet’s music tags, then add labeled samples of rare genres. Always verify licensing terms—some datasets restrict commercial use or require attribution.