AI Music Detection¶

Detect AI-generated music.

Endpoint¶

Method	URI	Description
POST	`/api/v1/ai-detection/{model_name}`	Detect AI-generated music

Path Parameters¶

Name	Type	Required	Description
`model_name`	string	Yes	Model to use: `lite`, `standard`, `pro`

Request Body¶

Field	Type	Required	Description
`file`	binary	Yes	Audio file to analyze (mp3, wav, flac, m4a, aac, ogg)

Models¶

Model	Processing Time	Price	Description
`lite`	30-40 seconds	$0.10	Single model, fast detection
`standard`	30~40 seconds	$0.20	model ensemble, balanced accuracy
`pro`	~1 minute	$0.50	model ensemble with detailed classification

Request Example¶

cURL¶

curl https://platform.mippia.com/api/v1/ai-detection/standard \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -X POST \
  -F "file=@/path/to/audio.mp3"

Python¶

import requests

url = "https://platform.mippia.com/api/v1/ai-detection/standard"
headers = {
    "Authorization": "Bearer YOUR_API_KEY"
}
files = {
    "file": open("/path/to/audio.mp3", "rb")
}

response = requests.post(url, headers=headers, files=files)
print(response.json())

Response (Initial)¶

{
  "task_id": "task_20251204052920_J8uNdq5z",
  "status": "pending",
  "filepath": "uploads/task_20251204052920_J8uNdq5z.mp3",
  "model": "standard",
  "created_at": "2025-12-04T05:29:20Z"
}

Callback Response (Processing)¶

{
  "task_id": "task_20251204052920_J8uNdq5z",
  "status": "processing",
  "result": null,
  "model_type": "standard"
}

Callback Response (Completed - Standard)¶

{
  "task_id": "task_20251204052920_J8uNdq5z",
  "status": "success",
  "model_type": "standard",
  "completed_at": "2025-12-04T05:30:15Z",
  "result": {
    "audio_filename.mp3": [
      {
        "overall_analysis": {
          "prediction": "real",
          "confidence": 0.923
        },
        "config": {
          "model_id": "model_0",
          "analysis_focus": "Waveform & Melody Pattern",
          "task": "Real/Fake Binary Classification",
          "num_classes": 2,
          "labels": ["real", "fake"]
        }
      },
      {
        "segment_analysis": {
          "prediction": ["real", "real", "real", "real"],
          "confidence": [0.891, 0.912, 0.887, 0.903]
        },
        "overall_analysis": {
          "prediction": "real",
          "confidence": 0.945
        },
        "config": {
          "model_id": "model_1",
          "analysis_focus": "Waveform & Melody Pattern",
          "task": "Real/Fake Binary Classification",
          "num_classes": 2,
          "labels": ["real", "fake"]
        }
      },
      {
        "segment_analysis": {
          "prediction": ["real", "real", "real", "real"],
          "confidence": [0.876, 0.901, 0.889, 0.912]
        },
        "overall_analysis": {
          "prediction": "real",
          "confidence": 0.934
        },
        "config": {
          "model_id": "model_2",
          "analysis_focus": "Waveform & Melody Pattern",
          "task": "Real/Fake Binary Classification",
          "num_classes": 2,
          "labels": ["real", "fake"]
        }
      },
      {
        "segment_analysis": {
          "prediction": ["real", "real", "real", "real"],
          "confidence": [0.902, 0.918, 0.895, 0.921]
        },
        "overall_analysis": {
          "prediction": "real",
          "confidence": 0.951
        },
        "config": {
          "model_id": "model_3",
          "analysis_focus": "Waveform & Melody Pattern",
          "task": "Real/Fake Binary Classification",
          "num_classes": 2,
          "labels": ["real", "fake"]
        }
      }
    ]
  }
}

Callback Response (Completed - Pro)¶

Pro model includes additional classifiers for detailed analysis:

{
  "task_id": "task_20251204052920_J8uNdq5z",
  "status": "success",
  "model_type": "pro",
  "completed_at": "2025-12-04T05:30:45Z",
  "result": {
    "audio_filename.mp3": [
      {
        "overall_analysis": {
          "prediction": "fake",
          "confidence": 0.876
        },
        "config": {
          "model_id": "model_0",
          "analysis_focus": "Waveform & Melody Pattern",
          "task": "Real/Fake Binary Classification",
          "num_classes": 2,
          "labels": ["real", "fake"]
        }
      },
      {
        "segment_analysis": {
          "prediction": ["fake", "fake", "ai_cover", "fake"],
          "confidence": [0.712, 0.834, 0.623, 0.789]
        },
        "overall_analysis": {
          "prediction": "fake",
          "confidence": 0.823
        },
        "config": {
          "model_id": "model_4",
          "analysis_focus": "Mixing & Audio Effects",
          "task": "Real/AI-Cover/Fake 3-Class",
          "num_classes": 3,
          "labels": ["real", "ai_cover", "fake"]
        }
      },
      {
        "segment_analysis": {
          "prediction": ["suno_v4", "suno_v4", "suno_v4_5", "suno_v4"],
          "confidence": [0.534, 0.612, 0.489, 0.567]
        },
        "overall_analysis": {
          "prediction": "suno_v4",
          "confidence": 0.634
        },
        "config": {
          "model_id": "model_5",
          "analysis_focus": "Waveform & Melody Pattern",
          "task": "Detailed Fake Source Classification",
          "num_classes": 6,
          "labels": ["real", "suno_v4", "suno_v4_5", "suno_v4_5_plus", "suno_v5", "other"]
        }
      },
      {
        "segment_analysis": {
          "prediction": ["fake", "fake", "fake", "fake"],
          "confidence": [0.891, 0.912, 0.878, 0.901]
        },
        "overall_analysis": {
          "prediction": "fake",
          "confidence": 0.912
        },
        "config": {
          "model_id": "model_6",
          "analysis_focus": "Waveform & Melody Pattern",
          "task": "Real/AI-Cover/Fake 3-Class",
          "num_classes": 3,
          "labels": ["real", "ai_cover", "fake"]
        }
      }
    ]
  }
}

Result Fields¶

Field	Type	Description
`task_id`	string	Unique task identifier
`status`	string	Task status: `pending`, `processing`, `success`, `failure`
`model_type`	string	Model used for detection
`completed_at`	string	ISO 8601 completion timestamp
`result`	object	Detection results keyed by filename

Result Structure¶

The result is an object where:

Key: Audio filename
Value: Array of model results

Each array element contains results from one model in the ensemble.

Model Result Fields¶

Field	Type	Description
`segment_analysis`	object	Results from analyzing individual segments (Optional, There is one model which has only segment_analysis!)
`overall_analysis`	object	Final prediction for the entire audio
`config`	object	Model configuration and metadata

Config Fields¶

Field	Type	Description
`model_id`	string	Unique model identifier (e.g., `model_0`, `model_1`)
`analysis_focus`	string	What the model analyzes (see Analysis Focus below)
`task`	string	Classification task type
`num_classes`	integer	Number of output classes
`labels`	array	Possible prediction labels

Analysis Focus¶

Focus	Description
Waveform & Melody Pattern	Analyzes audio waveform characteristics and melodic patterns
Mixing & Audio Effects	Analyzes mixing techniques and audio effect signatures

Analysis Result Fields¶

Field	Type	Description
`segment_analysis`	object	Results from analyzing individual segments of the audio
`segment_analysis.prediction`	array	Prediction for each audio segment
`segment_analysis.confidence`	array	Confidence score for each segment
`overall_analysis`	object	Final prediction considering the entire song structure
`overall_analysis.prediction`	string	Final aggregated prediction
`overall_analysis.confidence`	float	Final confidence score (0.0 - 1.0)

Classification Labels¶

Task	Labels
Real/Fake Binary	`real`, `fake`
3-Class	`real`, `ai_cover`, `fake`
Detailed Fake Source	`real`, `suno_v4`, `suno_v4_5`, `suno_v4_5_plus`, `suno_v5`, `other`

Notes¶

Supported formats: mp3, wav, flac, m4a, aac, ogg
Segment Analysis: Analyzes each segment of the audio independently to detect localized AI artifacts
Overall Analysis: Considers the entire song structure and aggregates segment results for final prediction
Result Array: Each element in the result array represents one model’s analysis, allowing clients to iterate through all model results easily