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YAML Metadata Warning:The task_ids "music-forensics" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
YAML Metadata Warning:The task_ids "audio-deepfake-detection" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
Acoustic Resonance AI Instrumental Music Forensics Benchmark
Dataset Description
Standardized, apples-to-apples forensic evaluation benchmarks for AcousticShield: AI Instrumental Music Sentry, developed for the Amazon Developer Hackathon 2026 (Alexa+ Track) and IEEE research paper submission.
Benchmark Files:
benchmark_instrumental_results.json: 20 matched pure instrumental compositions:- 10 Suno AI Pure Instrumentals: Jazz Duo (Piano & Guitar), Piano Trio Post-Bop, Baroque Strings, Cool Jazz Quartet from
Kukedlc/suno-ai-music-dataset. - 10 Authentic Classical Masters: Mozart (Piano K176, Adagio), Chopin (Prelude Op.28 No.16, Polonaises Op.26), Beethoven (Opus 122, Opus 13), Bach (Instrumental Pieces 0037, 0040, 0041) from
drengskapur/wav-classical-music. - Contains itemized ultrasonic cutoff frequencies (kHz), stereo coherence indices, vocoder comb spikes, and calibrated verdicts.
- 10 Suno AI Pure Instrumentals: Jazz Duo (Piano & Guitar), Piano Trio Post-Bop, Baroque Strings, Cool Jazz Quartet from
benchmark_results_music_empirical.json: Empirical multi-resolution STFT resonance and Haas phase dispersion records.benchmark_results_real_world_n60.json: 60-track in-the-wild audio validation cohort.benchmark_results_n1000.json: 1,000-trial Monte Carlo cross-channel evaluation.
Key Benchmark Metrics (N=20 Matched Cohort):
- Overall Accuracy: 95.0% (19 / 20 Correct)
- False Accusation Rate (FAR): 0.00% (100.0% Human Specificity on Classical Masters)
- AI Recall (TPR): 90.0% (9 / 10 Suno AI tracks detected)
- Mean Processing Latency: 40.8 ms (< 75 ms Alexa SLA)
Core Physical Invariances:
- Ultrasonic Brickwall Cutoff: Discrete neural codecs (EnCodec 48kHz, SoundStream) drop off sharply above 16.0 - 18.5 kHz, whereas physical classical instruments maintain natural acoustic overtones up to 22.05 kHz.
- Stereo Phase Coherence: Synthetic compositions exhibit mono-bleed phase collapse (coherence > 0.90), whereas acoustic room recordings feature natural Haas phase delays (25° - 75° dispersion).
- Multi-Resolution STFT Inversion Bottleneck: Codebook re-quantization produces an anomalous resonance surge (Delta SNR >= +6.8 dB).
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