Delivering seamless quality audio in real time using wireless technology is one of the great challenges facing the professional audio engineer. Wireless audio transfer has for some time been hampered by bandwidth constraints, coding delays and the introduction of bit errors, which cause significant degradation to the audio quality.
For live performance or live streaming audio, coding delay is also a prohibitive constraint that has actually prevented such applications as digital wireless microphones from entering the market. Coding delay also has implications for video applications where lip sync is required-for example a wireless stereo headset used in conjunction with a video iPod or mobile TV.
In order to take advantage of the improvements in wireless technology and bring it into the live transmission space, the industry requires a low coding delay algorithm, with compression to meet the bandwidth constraints but enough quality to reach the 100-dB dynamic range required by most live applications and quality audio products.
Audio quality
Sixteen-bit audio is regarded as the entry level for audio systems now on the market with a minimum sample rate of 44.1 kHz to match that of the venerable audio CD.
- Dynamic Range of 16-bit digital audio = 20 Log10 (216) = 96.32 dB
- Dynamic Range of 20-bit digital audio = 20 Log10 (220) = 120.4 dB
- Dynamic Range of 24-bit digital audio = 20 Log10 (224) = 144.5 dB
Taking CD audio quality as a benchmark, 16-bit, 44.1-kHz audio has a dynamic range of 96 dB. To achieve this level of dynamic range in bandwidth-limited applications such as Bluetooth stereo headsets, it will be necessary to use at least 16-bit audio as the raw input and then use a compression technology that can reproduce virtually all of the original dynamic range at the output.
Audio metrics for different bit resolutions
(Click on image to enlarge)
A challenge will be to find an algorithm that is able to deliver this quality level with very low latency. A greater challenge would be to use 24-bit audio while maintaining the low delay characteristic.
Coding delay
The main difficulty for live audio is the coding/decoding delay of the compression technology. While in most wired solutions the audio coding delay is masked by the lengthy video decoding delay, the wireless applications have no such luxury. The ability to lip sync to decoded video after having been encoded, packetized, passed over a wireless link, and then decoded is indeed a significant challenge.
In most applications the radio will have its own inherent characteristics and be bound by a standard. If we assume that it is fixed, and that the packing and unpacking of the RF protocol are fixed, that only leaves the audio compression to work with.
If we look at Bluetooth for example, it uses a series of transmission and reception time slots that are fixed in size and therefore have a limitation in terms of maximum bit rate and response time. The protocol also utilizes the ability to retransmit packets to correct errors in the transmitted stream.
If it was possible to minimize the re-transmissions needed by making a more robust algorithm, and also give it the ability to start the decoding process with only four encoded samples, then it should be possible to improve the response of the system.
Compression ratios
It can be said that all compression of audio results in some loss of audio content. The higher the compression ratio the more audio content is lost.
Both ADPCM (adaptive differential pulse code modulation) and perceptual codecs lose audio in some way during the encoding and decoding process. Perceptual codecs analyze the frequency spectrum and remove content deemed to be imperceptible to the human ear. The resultant audio is tuned to the human ear and thus sounds good even with the audio content removed.
This analysis requires a large audio sample (some 512 bytes) over which the analysis takes place. This is the source of the coding delay in many cases. The complexity of the audio can also affect the delay of the encoding process.
ADPCM codecs introduce other types of loss due to their own individual characteristics. The quantization process is by nature lossy and, depending on the accuracy of the linear predictor and inverse quantization used, can produce small errors in the reproduced audio. However, it does not remove audio content.
Because ADPCM does not analyze the audio spectrum, the processing delay is significantly less but produces the same dynamic range and preserves the audio content.
