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

43 engine classes, 3,830 source videos, after exclusions below:

Engine class	Files	Windows	Train	Test
2_rotor	18	3740	3372	368
h12	10	4863	3981	882
h2	92	56012	37044	18968
h4	112	40657	34450	6207
h6	97	84341	57384	26957
i2_180	127	88771	66385	22386
i2_180_two_stroke	41	9162	7007	2155
i2_270	169	134456	104910	29546
i2_360	63	24789	18457	6332
i2_360_two_stroke	44	12124	10577	1547
i3	133	57496	45440	12056
i3_two_stroke	10	3219	2943	276
i4	392	166630	131564	35066
i4_crossplane	134	68221	48907	19314
i4_diesel	75	31429	26944	4485
i5	115	29789	24849	4940
i5_diesel	52	21759	17435	4324
i6	157	49570	35274	14296
i6_diesel	52	29830	26165	3665
single_four_stroke	101	42045	31663	10382
single_two_stroke	94	44185	33465	10720
v10_72	154	63020	46769	16251
v10_90	14	11291	8406	2885
v12	112	37882	35178	2704
v16	8	4512	4512	0
v2_45	138	165815	146284	19531
v2_90	97	83045	73491	9554
v2_two_stroke	11	2425	1915	510
v4	80	31392	26760	4632
v4_two_stroke	35	8369	6995	1374
v6_120	72	31924	21840	10084
v6_60	212	61043	47627	13416
v6_90_even	58	36963	33490	3473
v6_90_odd	5	3446	2629	817
v6_diesel	16	3346	2502	844
v8_60	18	6307	5173	1134
v8_cross	264	139946	120989	18957
v8_diesel	124	45184	35857	9327
v8_flat	180	81189	68971	12218
v8_voodoo	65	34937	27993	6944
vr6	54	11260	6762	4498
w12	19	8787	7173	1614
w16	6	8552	8552	0

Class sizes are heavily imbalanced (5 to 392 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 5 (v6_90_odd) to 392 (i4).
• Two classes have zero test windows. v16 and w16 have only 8 and 6 source videos respectively; the per-video random 80/20 split happened to put every video from both classes into train. Don't evaluate on these classes without re-splitting.
• 97 ambiguous videos were excluded. Cross-referencing every video id against every engine_class it was scraped under found 97 YouTube 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, e.g. one video labeled both v2_90 and v4, another labeled across i4_diesel/i6_diesel/v10_90/v8_diesel simultaneously). All windows sourced from any of these videos were dropped entirely (110,830 of 1,994,553 rows, 5.6%) rather than guessing which label was correct. This hit some already-small classes hard: v2_two_stroke went from 19 to 11 files, h12 from 18 to 10, v10_90 from 19 to 14. The class table above already reflects these counts.
• 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.