Welcome back! In the previous lessons, you learned how to extract audio from video files, normalize audio, and split long recordings into smaller, manageable chunks using the Xabe.FFmpeg library in C#. These are essential skills for preparing audio for transcription or further analysis, especially when dealing with large or complex media files.

However, as you may have noticed, real-world audio is rarely perfect. Recordings often contain background noise, long periods of silence, and inconsistent volume levels. These issues can make transcription less accurate and slow down processing. To address these challenges, advanced audio preprocessing techniques are needed.

In this final lesson, you will learn how to use FFmpeg (through Xabe.FFmpeg) to reduce noise, remove silence, normalize volume, and compress audio files. You will also see how to combine these techniques for the best results. By the end of this lesson, you will be able to prepare high-quality audio files that are easier to transcribe and process, making your applications more robust and efficient.

Background noise is a common problem in audio recordings. It can come from air conditioners, traffic, or even just microphone hiss. This noise can make it harder for transcription services to understand speech, leading to errors or missed words.

To help with this, you can use the following method in your AudioProcessor class, which uses FFmpeg’s afftdn filter to reduce background noise in an audio file:

In the above method:

• noiseReduction: Controls the strength of noise reduction (higher values remove more noise but may affect audio quality).
• Uses FFmpeg’s afftdn filter to target and reduce steady background noise.

This method processes the input file and creates a new file with less background noise. This step is especially useful when working with recordings made in uncontrolled environments, such as interviews or meetings. By reducing noise before transcription, you can improve the accuracy of your results.

Once you’ve addressed background noise, the next challenge is dealing with long periods of silence that can disrupt processing and transcription.

Long periods of silence in audio files can cause problems for both transcription and processing. Silence can waste time, increase file size, and sometimes even confuse speech recognition systems. Removing these silent segments makes your audio files more efficient and easier to work with.

The following method in your AudioProcessor class uses FFmpeg’s silenceremove filter to cut out silence from an audio file:

Let's understand what each of these parameters does:

• silenceThreshold / stop_threshold: Sets the volume threshold (in dB) below which audio is considered silence and is used by FFmpeg to detect and remove silent segments.
• minSilenceDuration: This specifies the minimum duration (in seconds) that audio must remain below the threshold to be considered silence.
• stop_periods=-1: This tells FFmpeg to remove all periods of silence that match the criteria, not just a set number.
• stop_duration: This is set to the value of minSilenceDuration and works together with the threshold to define what counts as silence.
• detection=peak: This uses the peak level of the audio signal to detect silence, which is generally more accurate for speech.

This method produces a new audio file with most of the silent parts removed, making your audio more focused and efficient for further processing.

After removing silence, it’s important to ensure that the volume levels throughout your audio are consistent, which brings us to normalization.

Inconsistent volume levels are another common issue in audio recordings. Some parts may be too quiet, while others are too loud. This can make listening difficult and can also affect the accuracy of transcription services.

Audio normalization adjusts the volume of your audio so that it is consistent throughout the file. The following method in your AudioProcessor class uses FFmpeg’s loudnorm filter to achieve this:

This method:

• Uses FFmpeg’s loudnorm filter to automatically adjust and balance audio loudness.
• Converts audio to 16kHz mono PCM format, which is optimal for most speech recognition systems.

After normalization, your audio will have a more even volume, making it easier to listen to and process. It is good practice to normalize audio after reducing noise and removing silence, as this ensures the final file is as clear and consistent as possible.

Now that you’ve seen how each technique works individually, let’s look at how you can combine them for even better results.

While each preprocessing technique is useful on its own, you will often get the best results by combining them. For example, you might want to reduce noise, remove silence, and then normalize the audio — all in one workflow. This approach ensures your audio is clean, focused, and consistent before you send it for transcription or further analysis.

The following method in your AudioProcessor class combines noise reduction, silence removal, and normalization:

This combined approach is especially helpful when working with challenging recordings. By applying all three techniques, you can greatly improve the quality of your audio and make it much easier for transcription services to produce accurate results.

With your audio now clean and consistent, the final step is to consider file size and efficiency, which is where audio compression comes in.

Audio files can be large, especially after processing. Large files take longer to upload, download, and process. Compressing audio reduces file size, which speeds up these operations and saves storage space. Compression is especially important when working with cloud services or when you need to process many files quickly.

The following method in your AudioProcessor class uses FFmpeg to compress audio to a lower bitrate:

Keep in mind that compressing audio can reduce quality, so you should choose a bitrate that balances size and clarity for your needs. For speech, lower bitrates often work well without losing too much quality.

Now that you’ve learned how to preprocess and optimize your audio files, let’s summarize what you’ve accomplished and look ahead to applying these techniques in practice.

In this lesson, you learned how to use advanced audio preprocessing techniques with FFmpeg and Xabe.FFmpeg in C#. You saw how to reduce background noise, remove silence, normalize volume, and compress audio files. You also learned how to combine these techniques for the best results, making your audio files cleaner, smaller, and easier to transcribe.

These skills are essential for working with real-world audio, where quality and efficiency matter. By mastering these techniques, you can prepare your audio for accurate transcription and smooth processing, even when dealing with challenging recordings.

Now, you are ready to practice these techniques with hands-on exercises. In the next section, you will get a chance to apply what you have learned and see the results for yourself. Keep experimenting, and remember that good preprocessing is the key to great results in audio processing and transcription!