Audio Resampling in Android: What Is It & Why Is It Necessary

What is Audio Resampling?

Audio resampling is the process of changing the sample rate and bit depth of a digital audio signal. The sample rate refers to how many samples per second are used to represent the audio waveform. The bit depth is the number of bits used to store each sample, which determines the possible range of values (dynamic range) that can be represented.

Resampling changes these core parameters of the audio to convert it to a different sample rate and bit depth. For example, converting audio from a 44.1 kHz 16-bit signal to a 48 kHz 24-bit signal. This involves mathematically recalculating the waveform to have more or less samples per second and a wider or narrower range of sample values.

The purpose of resampling is typically to convert the audio into a sample rate or bit depth that is compatible with other systems or audio formats.[1]

Why is Audio Resampling Used?

Audio resampling is used for a few key reasons:

To allow audio playback at different sample rates – Audio is often recorded and stored at a specific sample rate like 44.1kHz. But the device playing the audio back may only support different rates like 48kHz or 32kHz. Resampling allows the audio to be converted to a compatible playback rate on the fly.

To optimize audio for different devices – Smartphones, speakers, TVs, and other devices can all have different audio hardware capabilities and playback requirements. Resampling allows the audio to be tailored and optimized for each device’s specifications.

To reduce file size – Higher sample rates require more data to store the audio. Resampling the audio to a lower rate can significantly reduce the file size without too much loss in quality.

By resampling the audio as needed, it allows maximum compatibility across devices, optimized playback, and smaller file sizes.

Audio Resampling in Android

Android supports audio playback at multiple sample rates like 44.1kHz, 48kHz, 96kHz, 192kHz etc. However, the audio hardware has a fixed sample rate it operates at. So Android needs to resample any audio that doesn’t match this hardware rate to make it compatible for playback.

For example, the audio DAC hardware in many Android devices operates at 48kHz. This means all audio played on the device, regardless of its original sample rate, needs to be resampled to 48kHz before reaching the audio hardware. Apps have no direct access to the audio hardware and can only send audio to Android’s audio framework which handles the necessary resampling.

As noted in discussions on Reddit, this resampling to 48kHz happens to all audio on many Android devices regardless of original quality. While convenient, resampling can impact audio quality and latency.

When Does Resampling Occur?

There are a few main cases when resampling occurs in digital audio:

Converting Between Audio Formats – When converting an audio file from one format to another that uses a different sample rate, such as converting a 96 kHz WAV file to a 44.1 kHz MP3 file, resampling is necessary to change the sample rate. As noted on the flyingSand blog, converting formats often requires resampling to avoid issues with playback at the new sample rate.

Mixing Audio Tracks – In digital audio workstations, resampling is commonly used when mixing tracks with different sample rates. As an example, mixing a 48 kHz track with a 44.1 kHz track requires resampling one of the tracks to match the project sample rate. Reddit users on r/edmproduction note resampling can pitch shift samples when mixing different sample rates.

Playback at New Sample Rate – Audio players may resample audio when playing it back at a sample rate different from the original file. As noted on Quora, this allows playback at different sample rates as needed by the audio output hardware.

Resampling During Audio Playback

The Android audio architecture consists of different layers that audio passes through before being output to the speakers or headphones. At the app layer, audio is played back at the native sample rate of the audio file, such as 44.1 kHz for CD quality audio. The audio is then passed to the Android AudioFlinger service, which mixes audio streams and sends the mixed audio to the hardware abstraction layer (HAL).

The HAL communicates directly with the audio driver and hardware on the device. It is at this stage that Android will resample the audio to a common sample rate across the system, which is often 48 kHz or 44.1 kHz depending on the device [1]. By resampling all audio to the same rate, the HAL can mix and process any audio streams together before sending to the hardware. This avoids issues with incompatible sample rates.

So in summary, on playback the app handles audio at its native rate, but at the system level Android will resample to a common rate before the audio reaches the speakers or headphones. Resampling happens at the hardware abstraction layer just before the audio driver.

Performing Resampling in Code

The Android AudioTrack class can be used to perform audio resampling in code. AudioTrack has methods like write() that allow providing audio data at a specific sample rate. By providing data at differing sample rates than the output sample rate, AudioTrack will handle resampling the data.

For high quality resampling, developers have a few options:

• Use the native Audio Resampler included in the Android NDK, which provides high quality resampling using FIR filters (1).
• Use third party libraries like libsamplerate that implement specialized resampling algorithms.
• Manually resample with custom code, being careful to properly bandlimit and avoid aliasing. This can be complex to implement well.

Overall, leveraging Android’s native audio resampling capabilities via AudioTrack is recommended, as it provides a simple API and high quality resampling out of the box.

Resampling and Audio Quality

Resampling an audio signal can potentially reduce its quality and fidelity. This is because resampling often utilizes interpolation techniques that are lossy in nature. As this source points out, inaccuracies can be introduced when an audio signal is resampled from one sample rate to another. The process of interpolation used to generate new sample values can act as a low pass filter, removing high frequency content from the original signal.

Whether resampling causes audible quality degradation depends on the specific resampling algorithm used. Some techniques like bandlimited sinc interpolation are designed to preserve quality as much as possible. However, most practical resampling implementations involve some tradeoff between efficiency and accuracy. According to discussions on Reddit, techniques used in digital audio workstations are generally transparent or near-transparent in terms of quality loss.

Lossless resampling methods do exist but tend to be computationally expensive. Simple truncation or nearest neighbor interpolation can resample audio without quality loss, but can introduce aliasing artifacts. In most cases, there is a tradeoff between efficiency, quality retention, and aliasing when performing audio resampling.

Optimization and Performance

Resampling can be CPU intensive as it requires transforming every sample in the audio stream (Floisand 2012). This can impact performance in apps, especially on mobile devices. However, there are some strategies to optimize resampling:

Apply resampling only when necessary, such as during playback or recording. Avoid resampling an audio stream multiple times (DSP Stack Exchange 2016).

Use efficient resampling algorithms and optimize code. Some options include linear interpolation, bandlimited sinc interpolation, and fast Fourier transform methods (Donat-Bouillud 2019).

When possible, resample offline rather than in real-time. This allows more processing time. Audio can be resampled and saved before being loaded into an app.

Utilize hardware acceleration if available. Some processors and DSPs provide optimized resampling functions.

Set thread priority appropriately on multi-core systems so audio processing does not starve other threads.

Test performance and optimize bottlenecks. Profile code to identify issues.

In many cases, the resampling process can be optimized to have minimal impact on overall app performance and user experience.

Special Considerations

When working with multi-channel audio, additional care needs to be taken during resampling to avoid phase issues or distortion. Most resampling algorithms are designed for stereo or mono audio, so multi-channel audio requires separate resampling of each discrete channel prior to mixing and playback.

Streaming audio also presents challenges, as the playback sample rate may differ from the source material. Resampling is often required to match the source to the playback rate. According to kevinboone.me, the audio quality loss from resampling 44.1 to 48 kHz during streaming is minimal and largely unnoticeable. However, more significant conversions could introduce audible distortion.

When working with streaming audio pipelines, developers should be mindful of minimizing unnecessary conversions, and using high quality resampling algorithms to preserve audio fidelity.

Conclusion

In summary, audio resampling in Android is necessary for several important reasons. As discussed, it allows audio to be converted to different sample rates and bit depths required by different audio systems and codecs. Resampling enables compatibility across various software and hardware components involved in audio playback and processing. Additionally, it can be used to optimize audio to improve performance on resource-constrained mobile devices like smartphones.

However, care must be taken when performing resampling to avoid degrading audio quality through introduction of artifacts like aliasing. Proper resampling techniques should be used, along with testing, to ensure high fidelity is retained as much as possible. Though some loss of quality is often unavoidable during conversion, resampling remains an essential process for the seamless functioning of audio pipelines and systems. Developers should aim to implement resampling efficiently while minimizing impacts on quality.