Tagging Audio at Scale
– ArtXTech
Right, Let’s Talk About the Soul-Crushing, Glorious Bullsh&$t of Tagging Audio at Scale.*
So, I’ve got a bloody mountain of audio files. Heaps of them. A soul-crushing pile of .wav and .mp3 files that need to be sorted, tagged, and made useful for people who just want to find a “happy, upbeat, indie rock track” for their kid’s birthday video.
In my case, though, this wasn’t for some corporate asset library. This whole soul-crushing exercise was the pre-processing, the proper hard labour soaked in bloodshot eyes, for a massive drum sound classification project. It was meant to be the grand finale, the big showpiece for my advanced computational analytics course. (You can read the full write-up on that whole saga at the link at the end of this page).
If you’ve ever worked in digital asset management at any real scale, you know the official pipeline is usually a shambles held together with Excel spreadsheets and the slowly dying will of a poor creative soul. I reckon I’d rather debug a WebGL memory leak in Internet Explorer 11 than manually tag 10,000 kick drum samples.
Let’s break down the “normal” way.
The “Standard” Workflow: A One-Way Ticket to Burnout City
This is the process built on three pillars of pain: a GUI, a spreadsheet, and a human ear that’s slowly going numb.
Step 1: The Auditioning & Sorting Arse-achery
You pop open a tool. Maybe Finder if you’re a savage, or something slightly more grown-up like Soundminer or Basehead if the company’s shelled out. The process is pure, painstaking manual labour.
- Click file. Play.
- Mutter to yourself, “Right, that’s a rock track. Bit bright, innit?”
- Drag the file into a folder you just made called Rock_Bright_Maybe_V2_Final.
- Repeat until your sanity frays and you start questioning your life choices.
Step 2: The Metadata Hellscape (feat. Excel)
Now for the real fun. You fire up a spreadsheet. It’s always a bloody spreadsheet.
graph TD
A[Listen to Audio File] --> B{Decide Genre/Mood};
B --> C[Open Google Sheet];
C --> D["Find Row for 'funky_bass_riff_07.wav'"];
D --> E["Click Dropdown -> 'Funk'"];
E --> F["Click Dropdown -> 'Groovy'"];
F --> G["Hand-type BPM -> '119'"];
G --> H{Notice typo: '110'};
H --> I[Swear at monitor];
I --> A;
Annnnnnd we do copy and paste eternally. We may use a separate, paid tool like ‘Mixed In Key’ to get the BPM and key, then manually transcribe the results into the spreadsheet. It’s a system practically designed for human error. Every typo, every subjective “vibe” difference, creates data debt that some other poor sod has to clean up later. It’s a digital pigsty.
Step 3: The DAW Chop Shop
Time to trim silence or normalise volume. You batch-load files into a DAW like Logic or Reaper. You’re still visually scanning waveforms, sanity-checking everything by ear because you just don’t trust the batch processor not to muck it up.
The whole thing is a fragile, unscalable nightmare. And when your boss (In this case, it was me - myself. I guess I am so genius to torture myself at the best quality) asks why it’s taking so long, you’re forced to say something that makes you sound both competent and irreplaceable.
- The Black Box Excuse: “Ah, you see, I’ve got a highly optimised local workflow. Custom macros in Soundminer, complex key bindings… my hands are just faster.” (Translation: It’s still just me, clicking like a madman.)
- The “Human Touch” Justification: “Well, an AI just can’t capture the vibe, can it? I use scripts to help, of course, but the final QA has to be my ear.”
We’re tech professionals in the 21st century, and we’re still managing massive data libraries like we’re organising a CD collection in 1998. It properly pisses me off. The worst part? I know there is no other way but just to do myself in order to make the source of truth data validation set.
The ‘Stop Mucking Around’ Pipeline: My Way
My brain, riddled with a delightful mix of ADHD and a deep love for elegant systems, looks at the mess above and sees a different path. A path paved with Python, data, and a healthy dose of spite-driven automation.
This isn’t about replacing the human ear. It’s about accomodating my stressed fogged brain. It’s about letting the machine do the grunt work so the human can do the actual creative curation.
Here’s the architecture. It’s what I’ve been building during my Master’s at Georgia Tech, escaping the corporate feature factory to do some proper hard works.
graph TD
subgraph "Phase 1: Ingestion & Feature Extraction (Python)"
A[Audio File Input] --> B(Librosa: Load & Decode);
B --> C{Spectral Analysis};
C --> D[Extract Features: MFCCs, Chroma, Spectral Contrast];
C --> E[Extract Metrics: BPM, RMS, Zero-Crossing];
end
subgraph "Phase 2: Classification & Tagging (ML & Logic)"
D --> F(Scikit-learn/TensorFlow Model);
F --> G[Predict Genre/Instrument];
E --> H(Logic-Based Rules);
H --> I[Assign Energy/Mood Tags];
end
subgraph "Phase 3: Output & Integration"
G & I --> J[Generate JSON Metadata];
J --> K(Inject into Asset DB / DAW);
A --> L[FFmpeg: Audio Segmentation/Normalisation];
L --> M(Output Processed Audio);
end
style K fill:#f9f,stroke:#333,stroke-width:2px
What does this tangled mess of a diagram actually do?
Instead of a human listening to a file and saying “sounds rocky,” the script does this:
def analyze_audio(file_path):
# Load the audio file
y, sr = librosa.load(file_path)
# Extract features that describe the 'timbre' and 'texture'
mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
# Get the beat, the 'vibe'
tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
# ...extract heaps more features (chroma, contrast, etc.)
# Now, feed these numbers into a pre-trained ML model
# predicted_genre = genre_model.predict([mfccs.mean(axis=1)])
metadata = {
'bpm': round(tempo),
'genre_prediction': "Rock", # predicted_genre[0],
'energy_level': "High" # Calculated from RMS, etc.
}
return metadata
# --- Run this on 10,000 files while you go make a coffee ---
# my_audio_metadata = analyze_audio('some_rock_track.wav')
# print(my_audio_metadata)
# >> {'bpm': 120, 'genre_prediction': 'Rock', 'energy_level': 'High'}
This pipeline takes the subjective, error-prone manual labour and turns it into a deterministic, scalable, and auditable system. It provides a baseline truth.
Now my job is to listen to the edge cases, fine-tune the models, and curate the final collections with your expert taste. Finally I am in the control room. AFTER MASSIVE TIME CONSUMING TUNING and TRAINING sorting models. Well, still it was totally worth it - otherwise the time that I would spent would be more than double.
This is where my decade of experience in UX engineering, data-driven CRO, and GRC meets my deep, romantic obsession with creative tech. It’s about building systems that are not only powerful but also elegant and actually bloody “useful”, without hallucinating fake productivity.
You wanna know how this pre-processed music data labelling has been actually used for the real project? Here is the article EN-Feature-Engineering-vs-CNNs-in-audio