# AI Music Detection *** ### What does AI Music Detection do? ACRCloud's AI Music Detection feature helps customers assess whether submitted audio content is more likely to be AI-generated or human-created. It is designed to support content inspection and risk assessment workflows. Detection results should be interpreted as model outputs based on statistical patterns in the audio, rather than as definitive proof of origin. *** ### How does AI Music Detection work? ACRCloud uses advanced, state-of-the-art detection technology to train specialized models for identifying whether audio content is AI-generated or human-created. For each inspected audio file, the system returns: * a binary prediction result * an `ai_probability` score * `source_probabilities` for supported source categories Both `ai_probability` and `source_probabilities` are generated directly by the model and are not manually adjusted or post-processed by human operators. *** ### What threshold determines `ai_generated` vs. `human`? The current threshold is **50%**. * If `ai_probability >= 50%`, the output is `ai_generated` * If `ai_probability < 50%`, the output is `human` *** ### Is AI Detection generally available? Yes. AI Detection is **generally available** and has been officially released. *** ### What performance data is available? The following figures are based on ACRCloud's internal evaluation dataset for the current model version. #### Binary Confusion Matrix (Human vs. AI) * **Precision (AI): 99.98%** * **Recall (AI): 99.94%** * **False Positive Rate: 0.06% of Human** * **False Negative Rate: 0.06% of AI** #### Per-Source Accuracy * **human**: 99.91% * **suno**: 99.86% * **udio**: 99.93% * **sonauto**: 98.81% * **mureka**: 99.99% * **rdiffusion**: 98.00% > These results reflect performance on internal test data and are provided for reference only. Actual performance may vary depending on audio quality, transformations, compression, mixing, editing, and real-world usage conditions. *** ### What source categories are currently supported? The currently supported source categories exposed in the platform include: * `human` * `suno` * `udio` * `sonauto` * `mureka` * `rdiffusion` We are actively expanding supported source coverage and are working to include additional model sources such as **ElevenLabs**, **MiniMax**, and **Seed-Music**. Supported source coverage may continue to evolve over time as generative audio models and market usage change. *** ### How should `ai_probability` and `source_probabilities` be interpreted? We recommend that customers interpret the output as follows: * Use the **binary prediction** for straightforward workflow decisions * Use **`ai_probability`** as a confidence reference * Use **`source_probabilities`** as supporting signals for source attribution analysis * Consider the result together with other contextual or operational signals where appropriate Both `ai_probability` and `source_probabilities` are direct outputs from the model itself. Detection results should not be treated as the sole basis for legal, enforcement, or other high-impact decisions without additional review. *** ### Notes * Detection results are probabilistic model outputs, not definitive proof of origin. * Performance may vary across different content types and real-world conditions. * Supported source categories may be updated over time as the model evolves. * Third-party model or product names are used only to describe detection coverage and do not imply partnership, endorsement, or affiliation. *** ### Suggested Disclaimer The AI Detection feature provides probabilistic model outputs based on patterns identified in submitted audio content. Results are intended to assist review and inspection workflows and should not be interpreted as definitive proof of content origin. Performance metrics shown in this document are based on internal evaluation data and may vary in real-world use cases. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.acrcloud.com/faq/ai-music-detection.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.