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Co-authored-by: Robin Doerfler <rdoerfler@users.noreply.huggingface.co>

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  1. CITATION.cff +42 -0
  2. LICENSE +409 -0
  3. README.md +231 -0
  4. USAGE.txt +114 -0
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CITATION.cff ADDED
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+ cff-version: 1.2.0
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+ message: "If you use this dataset, please cite it as below."
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+ type: dataset
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+ title: "Procedural Engine Sounds Dataset"
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+ authors:
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+ - family-names: "Doerfler"
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+ given-names: "Robin"
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+ orcid: "https://orcid.org/0009-0007-3904-1941"
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+ doi: "10.5281/zenodo.16883336"
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+ version: "1.0.0"
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+ date-released: 2025-08-21
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+ url: "https://doi.org/10.5281/zenodo.16883336"
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+ repository-code: "https://zenodo.org/records/16883336"
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+ license: "CC-BY-NC-4.0"
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+ keywords:
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+ - "audio"
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+ - "engine sounds"
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+ - "procedural generation"
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+ - "automotive"
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+ - "sound synthesis"
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+ - "mechanical sounds"
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+ - "vehicle acoustics"
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+ abstract: |
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+ The Procedural Engine Sounds Dataset is a comprehensive collection of
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+ synthetically generated and annotated engine audio samples. This dataset
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+ contains procedurally generated engine sounds with detailed annotations
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+ encoded as 4-channel audio, designed for research in audio processing,
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+ vehicle acoustics, and synthetic sound generation. The dataset includes
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+ 5,935 high resolution audio files (48 kHz, 16 bit) totalling ~19 hours
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+ and 24.47 GB of content across 8 distinct sets, with RPM and torque
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+ information embedded as continuous audio signals alongside stereo engine sounds.
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+ preferred-citation:
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+ type: dataset
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+ title: "Procedural Engine Sounds Dataset"
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+ authors:
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+ - family-names: "Doerfler"
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+ given-names: "Robin"
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+ orcid: "https://orcid.org/0009-0007-3904-1941"
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+ year: 2025
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+ publisher: "Zenodo"
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+ doi: "10.5281/zenodo.16883336"
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+ url: "https://doi.org/10.5281/zenodo.16883336"
LICENSE ADDED
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+ =======================================================================
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+
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+ Creative Commons is not a party to its public
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+ will be considered the “Licensor.” The text of the Creative Commons
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+ public licenses is dedicated to the public domain under the CC0 Public
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+ Domain Dedication. Except for the limited purpose of indicating that
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+ material is shared under a Creative Commons public license or as
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+ otherwise permitted by the Creative Commons policies published at
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+ creativecommons.org/policies, Creative Commons does not authorize the
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+ use of the trademark "Creative Commons" or any other trademark or logo
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+ without limitation, in connection with any unauthorized modifications
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+ Creative Commons may be contacted at creativecommons.org.
README.md ADDED
@@ -0,0 +1,231 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ pretty_name: Procedural Engine Sounds Dataset
3
+ task_categories:
4
+ - audio-to-audio
5
+ - audio-classification
6
+ - automatic-speech-recognition
7
+ tags:
8
+ - audio
9
+ - audio-dataset
10
+ - engine-sounds
11
+ - combustion-engine
12
+ - procedural-generation
13
+ - time-aligned
14
+ - rpm
15
+ - torque
16
+ - automotive
17
+ - nvh
18
+ - vehicle-acoustics
19
+ - sound-synthesis
20
+ - noise-free
21
+ size_categories:
22
+ - 10B<n<100B
23
+ license: cc-by-nc-4.0
24
+ ---
25
+
26
+
27
+ # Procedural Engine Sounds Dataset
28
+
29
+ ## Dataset Description
30
+
31
+ The Procedural Engine Sounds Dataset is a comprehensive collection of synthetically generated and annotated engine audio samples. This dataset contains procedurally generated high-resolution engine sounds free of confounding noises, with detailed time-aligned annotations, designed for research in audio processing, vehicle acoustics, and synthetic sound generation.
32
+
33
+ ## Associated Publication
34
+
35
+ The dataset and generation methodology are described in the following paper:
36
+
37
+ Doerfler, R., & Wyse, L. (2026).
38
+ *Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations.*
39
+ https://arxiv.org/abs/2603.07584
40
+
41
+ ## Dataset Details
42
+
43
+ ### Dataset Summary
44
+
45
+ - **Repository:** procedural-engine-sounds
46
+ - **Version:** 1.0
47
+ - **Publication Year:** 2025
48
+ - **License:** CC BY-NC 4.0
49
+
50
+ ### Research Applications
51
+
52
+ - **Audio Generation**: Train models to generate realistic engine sounds conditioned to time-varying engine operation states
53
+ - **Audio Classification**: Predict RPM and Engine Torque based on audio signals
54
+ - **Audio Analysis**: Research vehicle acoustics and engine sound patterns
55
+ - **Sound Synthesis**: Develop procedural audio generation techniques
56
+ - **Data Augmentation**: Use as augmentation material for in-cabin speech detection and recognition, noise suppression or other related tasks
57
+
58
+ ### Technical Specifications
59
+
60
+ This dataset contains only audio signals - no textual or linguistic content. Both the engine sounds and annotations (RPM/torque information) are provided as audio signals at 48 kHz sample rate in WAV format.
61
+
62
+ ## Dataset Structure
63
+
64
+ ### Data Organization
65
+
66
+ The dataset is organized into 8 distinct sets with two categories:
67
+
68
+ **Full Sets** (A, B, C, D):
69
+ - 3,068 files total (across 4 sets)
70
+ - ~9.83 hours of audio total
71
+ - ~12.65 GB total
72
+ - **Per set (average): ~767 files, ~2.46 hours, ~3.16 GB**
73
+
74
+ **Large Sets** (E, F, G, H):
75
+ - 2,867 files total (across 4 sets)
76
+ - ~9.18 hours of audio total
77
+ - ~11.82 GB total
78
+ - **Per set (average): ~717 files, ~2.30 hours, ~2.96 GB**
79
+
80
+ **Dataset Total: 5,935 files, ~19.01 hours, ~24.47 GB**
81
+
82
+ ### File Organization
83
+
84
+ ```
85
+ README.txt (this file)
86
+ USAGE.txt (quick start guide)
87
+ audio/
88
+ ├── A_full_set/ (767 files, ~2.46 hours, ~3.16 GB)
89
+ ├── B_full_set/ (767 files, ~2.46 hours, ~3.16 GB)
90
+ ├── C_full_set/ (767 files, ~2.46 hours, ~3.16 GB)
91
+ ├── D_full_set/ (767 files, ~2.46 hours, ~3.16 GB)
92
+ ├── E_large_set/ (717 files, ~2.30 hours, ~2.96 GB)
93
+ ├── F_large_set/ (717 files, ~2.30 hours, ~2.96 GB)
94
+ ├── G_large_set/ (717 files, ~2.30 hours, ~2.96 GB)
95
+ └── H_large_set/ (717 files, ~2.30 hours, ~2.96 GB)
96
+ metadata/
97
+ ├── A_full_set_summary.json
98
+ ├── A_full_set_stats.csv
99
+ ├── B_full_set_summary.json
100
+ ├── B_full_set_stats.csv
101
+ └── … (16 metadata files total)
102
+ ```
103
+
104
+ ### File Formats
105
+
106
+ **Audio Files:**
107
+ - Format: WAV
108
+ - Sample Rate: 48 kHz
109
+ - Channels: 4 (quad-channel)
110
+ - Bit Depth: 16 bit
111
+
112
+ **Metadata Files:**
113
+ - Summary files: JSON format
114
+ - Statistics files: CSV format (comma-separated values)
115
+
116
+ ### Data Structure
117
+
118
+ Each audio file contains **4-channel audio** at 48 kHz sample rate:
119
+ - **Channel 1-2**: Stereo engine sound audio
120
+ - **Channel 3**: Engine speed (RPM × 0.0001) as continuous audio signal
121
+ - **Channel 4**: Engine torque (Nm × 0.001) as continuous audio signal
122
+
123
+ ### Metadata Structure
124
+
125
+ #### Summary Files (.json)
126
+ Per-set statistics including:
127
+ - `num_files`: Number of audio files in set
128
+ - `total_duration_*`: Duration in seconds/minutes/hours
129
+ - `total_size_gb`: Storage size in GB
130
+ - `rpm_distribution`: Statistical distribution (min, max, mean, std, percentiles)
131
+ - `torque_distribution`: Statistical distribution (min, max, mean, std, percentiles)
132
+
133
+ #### Statistics Files (.csv)
134
+ Per-file metrics with columns:
135
+ - `filename`: Audio file name
136
+ - `samplerate`: Sample rate (48 kHz)
137
+ - `duration_sec`: File duration in seconds
138
+ - `size_MB`: File size in megabytes
139
+ - `rpm_min/max/mean/std`: RPM statistics for the file
140
+ - `torque_min/max/mean/std`: Torque statistics for the file
141
+
142
+ ### Data Access
143
+
144
+ Each audio file is a standard WAV file containing a 4-channel audio array at 48 kHz sample rate. When loaded, you receive the raw multichannel audio data from which RPM and torque information can be extracted from channels 3 and 4 respectively.
145
+
146
+ ### Audio Signal Encoding
147
+
148
+ - **RPM Signal**: Channel 3 contains engine speed values in RPM scaled by 0.0001 (multiply by 10,000 to get actual RPM)
149
+ - **Torque Signal**: Channel 4 contains torque values in Newton meters scaled by 0.001 (multiply by 1,000 to get actual Nm)
150
+ - **Engine Audio**: Channels 1-2 contain the stereo procedural engine sound
151
+
152
+ ## Technical Requirements
153
+
154
+ To work with this dataset, you will need:
155
+ - Audio processing software capable of reading multi-channel WAV files
156
+ - Programming languages: Python (recommended with librosa, soundfile, or scipy), MATLAB, R, or similar
157
+ - For metadata: JSON and CSV reading capabilities
158
+
159
+ ## Dataset Creation
160
+
161
+ ### Source Data
162
+
163
+ All audio samples are synthetically generated using procedural audio synthesis techniques.
164
+ No real-world engine recordings were used for audio generation.
165
+ Dataset results were thoroughly analysed and compared to real world recordings to verify representativeness and similarity regarding engine order magnitudes and harmonic deviations.
166
+
167
+ ### Annotations
168
+
169
+ Annotations were created during the generation process, with additional manual verification for quality assurance.
170
+
171
+ ## Considerations for Using the Data
172
+
173
+ ### Social Impact of Dataset
174
+
175
+ This dataset enables research in:
176
+ - Automotive audio simulation
177
+ - Vehicle sound design
178
+ - Audio processing algorithms
179
+ - Synthetic data generation techniques
180
+
181
+ ### Discussion of Biases
182
+
183
+ As a synthetic dataset, it reflects the biases inherent in the procedural generation algorithms and may not capture all real-world engine sound variations.
184
+
185
+ ### Other Known Limitations
186
+
187
+ - Limited to procedurally generated sounds
188
+ - May not represent all engine types or acoustic environments
189
+ - Generated with empirically determined synthesis parameters, hence contains fictional engine types and exhaust pipe configurations
190
+
191
+ ## License and Usage
192
+
193
+ ### License
194
+
195
+ This dataset is released under CC BY-NC 4.0 license (Creative Commons Attribution-NonCommercial 4.0 International).
196
+
197
+ **Attribution Required**: Please cite this dataset in any research or publications.
198
+
199
+ ### Citation
200
+
201
+ ```bibtex
202
+ @dataset{doerfler_2025_procedural_engine_sounds,
203
+ author = {Doerfler, Robin},
204
+ title = {Procedural Engine Sounds Dataset},
205
+ month = {August},
206
+ year = 2025,
207
+ publisher = {Zenodo},
208
+ version = {1.0},
209
+ doi = {10.5281/zenodo.16883336},
210
+ url = {https://doi.org/10.5281/zenodo.16883336}
211
+ }
212
+
213
+ @misc{doerfler2026analysisdrivenproceduralgenerationengine,
214
+ title={Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations},
215
+ author={Robin Doerfler and Lonce Wyse},
216
+ year={2026},
217
+ eprint={2603.07584},
218
+ archivePrefix={arXiv},
219
+ primaryClass={cs.SD},
220
+ url={https://arxiv.org/abs/2603.07584},
221
+ }
222
+
223
+ ```
224
+
225
+ ## Contact
226
+
227
+ For questions or issues regarding the dataset, please use the Hugging Face repository discussion page or refer to the associated publication.
228
+
229
+ ## Acknowledgments
230
+
231
+ This dataset was created through procedural audio synthesis leveraging established principles from engine acoustics research, including engine order analysis, extended harmonic-plus-noise synthesis methodologies, and exhaust system resonance modeling. The synthesis methodology builds upon decades of foundational research in vehicle acoustics and internal combustion engine sound modeling. Special thanks to the digital signal processing and vehicle acoustics research communities for their foundational work that made this dataset possible.
USAGE.txt ADDED
@@ -0,0 +1,114 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ USAGE.txt - Procedural Engine Sounds Dataset
2
+ ==========================================
3
+
4
+ QUICK START GUIDE
5
+ -----------------
6
+
7
+ This dataset contains 4-channel WAV audio files with engine sounds and embedded RPM/torque data.
8
+
9
+ FILE STRUCTURE:
10
+ - audio/[A-H]_*_set/: WAV files organized in 8 sets
11
+ - metadata/: JSON summaries and CSV statistics for each set
12
+
13
+ AUDIO CHANNEL LAYOUT:
14
+ - Channel 1-2: Stereo engine sound audio
15
+ - Channel 3: RPM values (multiply by 10,000 for actual RPM)
16
+ - Channel 4: Torque values (multiply by 1,000 for actual Newton-meters)
17
+
18
+ BASIC USAGE EXAMPLES:
19
+
20
+ 1. PYTHON (with soundfile):
21
+ ```python
22
+ import soundfile as sf
23
+ import numpy as np
24
+
25
+ # Load a 4-channel audio file
26
+ audio, sr = sf.read('audio/A_full_set/engine_001.wav')
27
+
28
+ # Extract channels
29
+ engine_left = audio[:, 0] # Left engine audio
30
+ engine_right = audio[:, 1] # Right engine audio
31
+ rpm_signal = audio[:, 2] * 10000 # RPM values
32
+ torque_signal = audio[:, 3] * 1000 # Torque in Nm
33
+ ```
34
+
35
+ 2. PYTHON (with librosa):
36
+ ```python
37
+ import librosa
38
+
39
+ # Load specific channels
40
+ engine_audio, sr = librosa.load('audio/A_full_set/engine_001.wav',
41
+ sr=48000, mono=False)
42
+ # Result shape: (4, samples) - channels x samples
43
+ ```
44
+
45
+ 3. MATLAB:
46
+ ```matlab
47
+ [audio, fs] = audioread('audio/A_full_set/engine_001.wav');
48
+
49
+ engine_left = audio(:,1);
50
+ engine_right = audio(:,2);
51
+ rpm = audio(:,3) * 10000;
52
+ torque = audio(:,4) * 1000;
53
+ ```
54
+
55
+ METADATA ACCESS:
56
+
57
+ 1. Load set summary (JSON):
58
+ ```python
59
+ import json
60
+ with open('metadata/A_full_set_summary.json', 'r') as f:
61
+ summary = json.load(f)
62
+ print(f"Set A contains {summary['num_files']} files")
63
+ ```
64
+
65
+ 2. Load file statistics (CSV):
66
+ ```python
67
+ import pandas as pd
68
+ stats = pd.read_csv('metadata/A_full_set_stats.csv')
69
+ print(stats.head()) # View first few files' stats
70
+ ```
71
+
72
+ DATASET ORGANIZATION:
73
+ - Full Sets (A,B,C,D): ~767 files each, ~2.46 hours each
74
+ - Large Sets (E,F,G,H): ~717 files each, ~2.30 hours each
75
+ - Total: 5,935 files, ~19 hours, ~24.5 GB
76
+
77
+ TECHNICAL SPECS:
78
+ - Sample Rate: 48 kHz
79
+ - Format: WAV (uncompressed)
80
+ - Channels: 4 (quad)
81
+ - Total Duration: ~19.01 hours
82
+ - Total Size: ~24.47 GB
83
+
84
+ COMMON WORKFLOWS:
85
+
86
+ 1. AUDIO ANALYSIS:
87
+ - Load engine audio (channels 1-2)
88
+ - Analyze spectral content, harmonics
89
+ - Correlate with RPM/torque data (channels 3-4)
90
+
91
+ 2. MACHINE LEARNING:
92
+ - Use engine audio as input features
93
+ - Use RPM/torque as target labels
94
+ - Train regression or classification models
95
+
96
+ 3. AUDIO SYNTHESIS:
97
+ - Study relationship between RPM/torque and audio features
98
+ - Train generative models conditioned on engine parameters
99
+ - Use for data augmentation in automotive applications
100
+
101
+ REQUIREMENTS:
102
+ - Audio processing software supporting multi-channel WAV
103
+ - ~25 GB free disk space
104
+ - Python/MATLAB/R with audio processing libraries (recommended)
105
+
106
+ TROUBLESHOOTING:
107
+ - If files won't load: Check multi-channel WAV support in your software
108
+ - If values seem wrong: Remember to apply scaling (RPM×10000, Torque×1000)
109
+ - For large dataset: Consider loading files individually rather than all at once
110
+
111
+ LICENSE: CC BY-NC 4.0 (Attribution-NonCommercial)
112
+ CONTACT: doerflerrobin@gmail.com
113
+
114
+ For detailed documentation, see README.txt
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