joakes90/Auto_Engine_Classification

Engine Sound Windows (YouTube-derived, metadata-only)

Timestamps and weak labels for training an engine-configuration audio classifier (v-twin vs. inline-4 vs. flat-6, etc.) from short audio windows. This dataset does not contain audio. Each row points at a public YouTube video id plus a (start_sec, end_sec) window; you fetch and slice the audio yourself (see Reconstructing audio below).

Why metadata-only

The source audio was collected by searching YouTube (via yt-dlp) for engine-sound terms and downloading matching videos. The dataset author does not hold redistribution rights to that audio, so — following the precedent set by AudioSet, MusicCaps, and FSD50K for exactly this situation — only the video id, window timestamps, and derived labels are published here, not audio bytes. This also means the cc-by-sa-4.0 license above covers only this repository's metadata (ids, timestamps, labels); the underlying YouTube videos remain under their original creators' copyright and are not relicensed or redistributed by this dataset in any form.

Dataset structure

Column	Type	Meaning
window_id	string	Unique id for this window (winNNN), stable across the whole corpus
youtube_id	string	11-character YouTube video id (https://www.youtube.com/watch?v=<youtube_id>)
engine_class	string	Engine configuration label, e.g. v8_flat, i4_diesel, single_two_stroke
start_sec / end_sec	float	Window bounds within the source video, in seconds
contains_target	bool	See Label semantics
quality_flag	bool	See Label semantics
split	string	train or test — assigned per source video, so every window from one video stays in the same split

Windows are 2.0 seconds long with 1.0 second of step between consecutive windows (50% overlap), confirmed directly from the underlying manifest's timestamps.

Label semantics

contains_target and quality_flag are model-derived, not human-verified — they come from running panns_inference's AudioTagging model (trained on AudioSet) over each window and thresholding two sets of its 527 class scores:

• contains_target = True when the window's max score across a set of engine/vehicle AudioSet classes exceeds 0.585 — i.e. an engine sound was likely detected.
• quality_flag = True when the window's max score across a set of background-noise/contamination AudioSet classes exceeds 0.2 — i.e. contamination was likely detected.

quality_flag = True is a caution flag, not an endorsement — despite the name, it does not mean the window is good quality. Treat both columns as weak, noisy supervision (useful for filtering or as auxiliary features) rather than ground truth.

engine_class, by contrast, comes from which search query the source video was found under — also not independently verified per-video (see Known limitations).

Engine classes

34 engine classes, 3,877 source videos, after exclusions below:

Engine class	Files	Windows	Train	Test
2_rotor	141	43121	36452	6669
h12	10	4863	3026	1837
h2	92	56012	40333	15679
h4	112	40657	32253	8404
h6	97	84341	70911	13430
i2_180	127	88771	73716	15055
i2_180_two_stroke	41	9162	6487	2675
i2_270	169	134456	110712	23744
i2_360	63	24789	18944	5845
i2_360_two_stroke	44	12124	11617	507
i3	133	57496	45909	11587
i4	394	168553	140949	27604
i4_crossplane	134	68221	54634	13587
i4_diesel	76	31606	27721	3885
i5	115	29789	22419	7370
i5_diesel	52	21759	19513	2246
i6	157	49570	40540	9030
i6_diesel	52	29830	11117	18713
single_four_stroke	101	42045	29771	12274
single_two_stroke	94	44185	35973	8212
v10_72	156	63068	51501	11567
v12	112	37882	26548	11334
v2_45	138	165815	144387	21428
v2_90	97	83045	75219	7826
v4	80	31392	22601	8791
v4_two_stroke	41	9750	7735	2015
v6_120	72	31924	24099	7825
v6_60	211	60753	48324	12429
v6_90_even	76	43334	36442	6892
v8_cross	265	140562	111631	28931
v8_diesel	126	47716	30052	17664
v8_flat	180	81189	65364	15825
v8_voodoo	65	34937	24634	10303
vr6	54	11260	10374	886

Class sizes are heavily imbalanced (10 to 394 files per class) — account for this when sampling/weighting during training.

Known limitations

• Weak, auto-derived labels. contains_target/quality_flag come from an AudioSet-trained tagger's thresholded scores, not human review (see Label semantics).
• Class imbalance. File counts per class range from 10 (h12) to 394 (i4).
• All classes have representation in both train and test sets (no class has zero test windows after conflict exclusions).
• Cross-class conflicts. Cross-referencing every video id against every engine_class it was scraped under found videos that had been pulled into more than one conflicting engine_class (almost certainly multi-engine compilation/comparison videos caught by more than one search query). All windows sourced from any of these videos were dropped entirely (99,105 of 1,983,082 rows, 5.0%) rather than guessing which label was correct. This also resulted in 9 engine classes being dropped entirely from the export (they had no videos that didn't also appear in at least one conflicting class). The class table above reflects the final, post-conflict list.
• Link rot. Since only YouTube ids are published (see Why metadata-only), some fraction of source videos will become unavailable over time as creators delete or privatize them — unlike a self-hosted audio dataset, this one can shrink on its own.
• engine_class isn't independently verified per video beyond the cross-class-conflict check above — a video could still be mislabeled by its original search query in a way that doesn't produce a detectable cross-class conflict (e.g. a single video mislabeled but never scraped under any other class).

Reconstructing audio

For a given row, download the source video's audio and trim to the window:

yt-dlp -f bestaudio -x --audio-format m4a \
  "https://www.youtube.com/watch?v=<youtube_id>" -o source.m4a

ffmpeg -i source.m4a -ss <start_sec> -to <end_sec> -c copy window.m4a

For batch reconstruction, group rows by youtube_id first so each video is downloaded once regardless of how many windows come from it.

License and usage

The labels, timestamps, and ids in this repository are released under cc-by-sa-4.0. This does not extend any rights to the underlying YouTube video content, which remains the property of its original creators — this dataset does not redistribute, host, or relicense that audio. Commercial users should independently verify their own right to use any audio they fetch via the ids in this dataset.