Audio Quality Checker

Analyze audio quality, detect noise types, and provide improvement recommendations. Use when users need to check audio quality, validate recordings, or identify audio problems.

SenseAudio Audio Quality Checker

Analyze audio files to detect quality issues, identify noise types, and provide improvement recommendations.

What This Skill Does

• Detect noise and quality issues in audio files
• Identify specific noise types (background noise, hum, static, etc.)
• Provide noise severity ratings
• Generate quality assessment reports
• Recommend improvements for audio quality

Prerequisites

Install required Python packages:

bash

pip install requests

Implementation Guide

Step 1: Analyze Audio File

python

import os
import requests

API_KEY = os.environ["SENSEAUDIO_API_KEY"]

def check_audio_quality(audio_file):
    url = "https://api.senseaudio.cn/v1/audio/analysis"

    headers = {"Authorization": f"Bearer {API_KEY}"}
    files = {"file": open(audio_file, "rb")}
    data = {"model": "sense-asr-check"}

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

# Example response:
# {
#   "audio_info": {
#     "duration": 5230,
#     "format": "wav"
#   },
#   "result": {
#     "has_noise": true,
#     "noise_score": 0.65,
#     "severity": "moderate",
#     "noise_types": ["background_noise", "hum"],
#     "analysis": "Audio contains moderate background noise and electrical hum"
#   }
# }

Step 2: Parse Quality Report

python

def parse_quality_report(analysis_result):
    audio_info = analysis_result.get("audio_info", {})
    result = analysis_result.get("result", {})

    report = {
        "duration_ms": audio_info.get("duration"),
        "format": audio_info.get("format"),
        "has_issues": result.get("has_noise", False),
        "noise_score": result.get("noise_score", 0),
        "severity": result.get("severity", "none"),
        "noise_types": result.get("noise_types", []),
        "analysis": result.get("analysis", ""),
        "recommendations": generate_recommendations(result)
    }

    return report

Step 3: Generate Recommendations

python

def generate_recommendations(quality_result):
    recommendations = []

    noise_score = quality_result.get("noise_score", 0)
    noise_types = quality_result.get("noise_types", [])
    severity = quality_result.get("severity", "none")

    # General recommendations based on severity
    if severity == "severe":
        recommendations.append("Re-record audio in a quieter environment")
        recommendations.append("Use professional audio equipment")
    elif severity == "moderate":
        recommendations.append("Apply noise reduction in post-processing")
        recommendations.append("Consider using a better microphone")
    elif severity == "mild":
        recommendations.append("Minor noise reduction may improve quality")

    # Specific recommendations based on noise types
    if "background_noise" in noise_types:
        recommendations.append("Record in a quieter location")
        recommendations.append("Use acoustic treatment (foam panels, curtains)")

    if "hum" in noise_types:
        recommendations.append("Check for electrical interference")
        recommendations.append("Use balanced audio cables")
        recommendations.append("Ensure proper grounding of equipment")

    if "static" in noise_types:
        recommendations.append("Check cable connections")
        recommendations.append("Replace faulty cables or equipment")

    if "wind_noise" in noise_types:
        recommendations.append("Use a windscreen or pop filter")
        recommendations.append("Record indoors or in sheltered location")

    if "echo" in noise_types:
        recommendations.append("Add acoustic treatment to reduce reflections")
        recommendations.append("Record closer to microphone")

    return recommendations

Step 4: Batch Quality Check

python

def batch_quality_check(audio_files):
    results = []

    for audio_file in audio_files:
        try:
            analysis = check_audio_quality(audio_file)
            report = parse_quality_report(analysis)

            results.append({
                "file": audio_file,
                "status": "analyzed",
                "report": report
            })
        except Exception as e:
            results.append({
                "file": audio_file,
                "status": "error",
                "error": str(e)
            })

    return results

Advanced Features

Quality Scoring System

python

def calculate_quality_score(analysis_result):
    """Calculate overall quality score (0-100)"""
    noise_score = analysis_result.get("noise_score", 0)
    severity = analysis_result.get("severity", "none")

    # Base score
    base_score = 100

    # Deduct based on noise score
    noise_penalty = noise_score * 50  # Max 50 points

    # Additional penalty for severity
    severity_penalties = {
        "none": 0,
        "mild": 10,
        "moderate": 25,
        "severe": 40
    }
    severity_penalty = severity_penalties.get(severity, 0)

    final_score = max(0, base_score - noise_penalty - severity_penalty)

    return {
        "score": round(final_score, 1),
        "grade": get_quality_grade(final_score)
    }

def get_quality_grade(score):
    if score >= 90:
        return "Excellent"
    elif score >= 75:
        return "Good"
    elif score >= 60:
        return "Fair"
    elif score >= 40:
        return "Poor"
    else:
        return "Unacceptable"

Comparative Analysis

python

def compare_audio_quality(original_file, processed_file):
    """Compare quality before and after processing"""

    original_analysis = check_audio_quality(original_file)
    processed_analysis = check_audio_quality(processed_file)

    original_score = calculate_quality_score(original_analysis["result"])
    processed_score = calculate_quality_score(processed_analysis["result"])

    comparison = {
        "original": {
            "file": original_file,
            "score": original_score,
            "noise_score": original_analysis["result"]["noise_score"]
        },
        "processed": {
            "file": processed_file,
            "score": processed_score,
            "noise_score": processed_analysis["result"]["noise_score"]
        },
        "improvement": {
            "score_delta": processed_score["score"] - original_score["score"],
            "noise_reduction": original_analysis["result"]["noise_score"] - processed_analysis["result"]["noise_score"]
        }
    }

    return comparison

Automated Quality Gate

python

def quality_gate_check(audio_file, min_score=70):
    """Check if audio meets minimum quality threshold"""

    analysis = check_audio_quality(audio_file)
    quality = calculate_quality_score(analysis["result"])

    passed = quality["score"] >= min_score

    return {
        "passed": passed,
        "score": quality["score"],
        "grade": quality["grade"],
        "threshold": min_score,
        "analysis": analysis
    }

Detailed Report Generation

python

def generate_detailed_report(audio_file):
    """Generate comprehensive quality report"""

    analysis = check_audio_quality(audio_file)
    report = parse_quality_report(analysis)
    quality = calculate_quality_score(analysis["result"])

    detailed_report = f"""
# Audio Quality Report

## File Information
- **File**: {audio_file}
- **Duration**: {report['duration_ms']/1000:.2f} seconds
- **Format**: {report['format']}

## Quality Assessment
- **Overall Score**: {quality['score']}/100
- **Grade**: {quality['grade']}
- **Noise Score**: {report['noise_score']:.2f}
- **Severity**: {report['severity']}

## Issues Detected
{'- ' + '\\n- '.join(report['noise_types']) if report['noise_types'] else 'No issues detected'}

## Analysis
{report['analysis']}

## Recommendations
{'- ' + '\\n- '.join(report['recommendations']) if report['recommendations'] else 'No recommendations'}
"""

    return detailed_report

Use Cases

Pre-Recording Validation

python

def validate_recording_environment():
    """Test recording environment before actual recording"""

    # record_test_audio() is a placeholder — implement using your preferred
    # audio capture library (e.g. sounddevice, pyaudio) to record a short clip.
    test_file = record_test_audio(duration=5)

    # Check quality
    gate_result = quality_gate_check(test_file, min_score=75)

    if gate_result["passed"]:
        return {
            "ready": True,
            "message": "Environment is suitable for recording"
        }
    else:
        return {
            "ready": False,
            "message": "Environment needs improvement",
            "recommendations": gate_result["analysis"]["result"]["recommendations"]
        }

Post-Production Quality Control

python

def qc_pipeline(audio_files, min_score=80):
    """Quality control for batch of audio files"""

    qc_results = {
        "passed": [],
        "failed": [],
        "total": len(audio_files)
    }

    for audio_file in audio_files:
        result = quality_gate_check(audio_file, min_score)

        if result["passed"]:
            qc_results["passed"].append(audio_file)
        else:
            qc_results["failed"].append({
                "file": audio_file,
                "score": result["score"],
                "issues": result["analysis"]["result"]["noise_types"]
            })

    qc_results["pass_rate"] = len(qc_results["passed"]) / qc_results["total"] * 100

    return qc_results

Output Format

• Quality analysis JSON
• Detailed quality report (Markdown/PDF)
• Noise detection results
• Improvement recommendations
• Quality score and grade

Tips for Best Results

• Test audio files before important recordings
• Use quality gates in production pipelines
• Compare before/after processing
• Track quality metrics over time
• Address severe issues immediately

Example Usage

User request: "Check the quality of this audio recording and tell me if it's good enough for a podcast"

Skill actions:

1. Upload audio file to analysis API
2. Parse quality results
3. Calculate quality score
4. Generate recommendations
5. Provide clear pass/fail assessment
6. Suggest improvements if needed

Reference

API docs: https://senseaudio.cn/docs/speech_recognition