The Importance of Preprocessing

Preprocessing is not merely a preliminary stage of model development—it is a decisive factor that can shape the success or failure of an audio-based ML system. Raw audio data is usually captured in uncontrolled or semi-controlled environments. The result? Unpredictable variables such as wind, traffic, phone interference, echo, and inconsistent speaker behaviour.

Without cleaning, the following issues can arise:

• Models may misidentify phonemes due to background distortion.
• Speech boundaries become blurred, affecting time-aligned transcriptions.
• Speaker diarisation becomes unreliable due to overlapping or muffled voices.
• Accents and dialects may be underrepresented due to poor signal clarity.
• Silence padding can distort token duration and affect model rhythm.

From a machine learning standpoint, unclean data introduces noise in the truest sense—signals that the model must learn to ignore. This forces the model to waste capacity trying to make sense of irrelevant input. Worse, it might learn incorrect patterns, reducing generalisation when tested in real-world scenarios.

Effective preprocessing:

• Enhances model training speed.
• Improves generalisation across domains and languages.
• Reduces the burden on annotators or downstream processors.
• Increases accuracy for speech-based applications.

Whether the data is for English, isiZulu, Swahili, or French, the same principle applies: the cleaner the audio, the more reliable the outcome.

Common Cleaning Techniques

The methods used to clean raw speech data vary depending on the intended application, but several key techniques are standard across most workflows. Each method targets specific issues in the recording, from noise and silence to amplitude and clipping.

Noise Reduction

Noise is one of the most disruptive elements in audio recordings. It could be ambient hum, hiss, chatter, or even electrical interference from poor recording equipment.

The goal of noise reduction is to isolate the actual speech signal from the background interference. Common techniques include:

• Spectral subtraction, which estimates noise profiles and subtracts them from the waveform.
• Adaptive filtering, where the filter changes over time to follow the noise pattern.
• Deep learning-based noise suppressors, which learn to separate speech from noise using trained models.

Silence Trimming

Silence padding at the beginning or end of clips adds unnecessary duration. In ASR systems, this can introduce delays in processing or misalignment with transcriptions. Silence detection algorithms locate periods where signal energy drops below a threshold and remove them, creating more efficient files and faster training loops.

Volume Normalisation

Recordings from different sources or devices rarely maintain consistent loudness. This inconsistency can lead to bias in ML models that assume equal audio intensity across samples. Normalisation adjusts the gain so all files peak at the same amplitude, or follow a loudness standard like -23 LUFS. This also improves the balance between soft and loud speakers, ensuring equal representation in learning.

Clipping and Hiss Filtering

Clipping is caused when the recorded sound exceeds the microphone’s maximum input level, producing a distorted and flattened waveform. This not only degrades quality but can also confuse learning algorithms. Declipping techniques reconstruct the waveform using prediction methods. Hiss, often found in low-quality recordings, is a constant high-frequency noise that can be reduced with high-shelf filters or spectral denoisers.

Bandpass Filtering

Since human speech predominantly lies in the 300 Hz to 3.4 kHz range, bandpass filters can help eliminate frequencies outside this range. This removes unnecessary low-frequency rumble (e.g. foot tapping) and high-frequency hiss (e.g. fan noise), making speech more intelligible.

Format Standardisation

Standardising your audio format ensures compatibility with your toolchain and model input requirements. Typically, audio is converted to:

• WAV format (lossless)
• Mono channel (to simplify signal analysis)
• 16 kHz or 8 kHz sample rate (depending on task)

Metadata Tagging

Clean audio without proper labelling can still be ineffective. Each file should be tagged with speaker ID, language, dialect, recording device, environment condition, and intended usage. Metadata ensures the cleaned audio is not only clear but also contextually meaningful.

Toolkits and Pipelines

Manually processing each audio file is unfeasible in any dataset beyond a few dozen clips. Fortunately, various audio preprocessing tools and libraries have emerged to help engineers manage audio data effectively, whether for small projects or enterprise-scale deployments.

SoX (Sound eXchange)

SoX is a powerful command-line utility for audio processing. It supports trimming, resampling, noise profiling, and format conversion. It is scriptable, lightweight, and can be integrated into batch pipelines.

Examples of SoX commands include:

• Trimming: sox input.wav output.wav trim 0 30
• Resampling: sox input.wav -r 16000 output.wav
• Normalising: sox input.wav output.wav gain -n

Audacity with Scripting

Audacity is widely known for manual audio editing, but also supports scripting via Nyquist and mod-script-pipe extensions. While it’s not ideal for large-scale automation, it’s excellent for prototyping or semi-automated cleaning workflows.

Kaldi Recipes

Kaldi is a research-focused speech recognition toolkit used globally. It contains comprehensive recipes for ASR systems, including VAD, MFCC extraction, and pre-cleaning routines. It integrates well with Bash and Python scripts.

Python-Based Libraries

• Librosa: A Python library for analysing audio and music. Provides tools for trimming, spectrogram generation, feature extraction, and tempo analysis.
• PyDub: Simplifies conversion and segmenting of audio files, ideal for smaller preprocessing tasks.
• SciPy & NumPy: Enable low-level audio manipulation, filtering, and mathematical transformations on waveforms.
• OpenSMILE: Designed for feature-rich speech processing, it supports tasks like emotion detection, prosody extraction, and signal diagnostics.

FFmpeg

FFmpeg is essential for format conversions, re-encoding, and stereo-to-mono processing. It can also help isolate audio from video sources and includes dozens of filters for cleaning and enhancement.

Having a consistent and modular toolkit makes it easier to adapt your pipeline as data sources change, ensuring long-term maintainability.

Quality Assurance and Validation

Once audio has been cleaned, it must be verified to ensure that preprocessing hasn’t degraded its usability. Quality assurance (QA) is not an optional step—it safeguards your machine learning investments.

Spectrogram Analysis

Visual inspection using spectrograms can highlight issues like over-suppression, missing frequencies, or unwanted artefacts. Tools like Audacity, Sonic Visualiser, and Matplotlib (with Librosa) allow you to inspect time-frequency plots.

Signal-to-Noise Ratio (SNR)

A numerical approach to measuring cleaning success, SNR compares the strength of the speech signal to background noise. Higher SNR indicates a cleaner signal. However, artificially inflated SNR (from over-cleaning) can also make the speech sound unnatural, so this should be balanced with subjective evaluation.

PESQ and STOI Scores

These are standardised perceptual metrics used to measure speech quality and intelligibility. PESQ (Perceptual Evaluation of Speech Quality) is ideal for telephony-based applications. STOI (Short-Time Objective Intelligibility) assesses how understandable speech remains after enhancement.

Spot Checks and Human Review

Nothing beats human ears when detecting over-filtered audio or unnatural edits. Perform random spot checks on a statistically significant portion of your cleaned dataset to ensure real-world usability. This is especially important for tonal languages or datasets with emotional speech.

Transcription Alignment Checks

If your data includes transcriptions, confirm they still match the audio after cleaning. Use alignment tools (e.g. Gentle or Aeneas) to test time-aligned accuracy.

Automating Preprocessing in Large Datasets

As data collection grows, cleaning needs to become part of a streamlined, automated process that can run at scale. This requires building infrastructure that enables reliable, reproducible, and scalable processing.

Batch Processing Scripts

Write custom shell or Python scripts that perform chained operations using SoX, FFmpeg, or Librosa. These scripts should handle large batches of files, validate outputs, and log any errors or quality issues.

Containerisation with Docker

Encapsulate your preprocessing environment using Docker so it can be deployed across cloud, local, or hybrid systems. Docker ensures consistency and avoids dependency issues.

Workflow Orchestration

Use tools like Apache Airflow, Snakemake, or Luigi to manage tasks, monitor progress, and schedule large jobs. These orchestration tools help manage dependencies and rerun failed jobs automatically.

Integration with CI/CD

Incorporate preprocessing into your data ingestion pipelines using Continuous Integration and Deployment practices. Automatically clean and validate incoming audio from user submissions, crowd-sourcing, or field studies.

Cloud Infrastructure

Leverage cloud services (e.g. AWS Lambda, Google Cloud Functions) to trigger preprocessing jobs when new data is uploaded. This enables near real-time ingestion and cleaning for enterprise use.

Audit Logging and Versioning

Maintain logs of cleaning actions performed, software versions used, and audio metrics. This ensures transparency and reproducibility for researchers, clients, or internal QA audits.

The result? A resilient, scalable preprocessing system that guarantees every piece of speech data is cleaned, verified, and ready for use in machine learning or product deployment.