First, a disclaimer: This description is by no means a full assessment of MQA. The technology incorporates teaching from fields as diverse as psychoacoustics, neuroscience, brain imaging, and information theory that are beyond my ken. A full explanation could fill a book.
MQA doesn’t tinker at the margins of digital audio’s limitations, but rather represents a departure from current thinking. Despite its radical approach, it operates within the domain of the existing technological and commercial digital-audio infrastructures.
The seemingly insurmountable barriers to better digital sound have been circumvented through elegant and imaginative thinking. For example, MQA—astonishingly—rethinks sampling and quantization (those twin pillars of digital audio) by implementing groundbreaking new sampling and quantizing techniques. Once inviolable “laws” of sampling theory, such as “a sampled system cannot convey time differences shorter than two sample periods,” are exposed as merely the conventional wisdom of an earlier age. Similarly, MQA reconsiders another cornerstone of digital audio, the brickwall filter. Meridian has developed a more sophisticated analysis of digital filtering, and of the aliasing the filter is designed to prevent.
The description below is thus a small sliver of the technology behind MQA—a sliver that on its own would be cause for celebration. But understand that, combined with other advances that are beyond the scope of this article, MQA is a revolution that comes along once in a lifetime. For this article I’ll explain just two aspects of MQA: one of the reasons it can sound better than any current “high-resolution” PCM or DSD system, and how an MQA file can convey that extraordinary sound quality in a stream roughly one tenth the size of conventional “hi-res” PCM. (For the technically minded, Stuart and Craven’s Audio Engineering Society paper explaining several aspects of MQA, “A Hierarchical Approach to Archiving and Distribution,” can be purchased from the Audio Engineering Society at aes.org, paper #9178.)
In my view, MQA’s most important characteristic is not that it can deliver high resolution at a low bit rate (although that aspect is what will gain it widespread adoption), but rather that it sounds better than any other format extant, analog or digital. Meridian maintains that the standard metrics for digital audio—sample rate and the number of bits in each sample—are far less important than two new metrics: 1) absolute stability of the noise floor and 2) the amount of “temporal blur” in the digital system. This last term describes the smearing of transient information over time by digital filters. Temporal blur affects the sounds’ timing precision. That is, music’s transient’s energy is spread out, appearing before and after the transient. Meridian has previously addressed this problem of filter “ringing” with its “apodizing” filter, a technique now commonly used. The apodizing filter doesn’t remove ringing (temporal blur); rather, it kills the incoming ringing and replaces it with its own ringing that occurs only after the transient, which is less sonically detrimental. MQA’s new techniques outright remove temporal blur rather than making it less sonically objectionable.
The steeper the filter, the greater the temporal blur. The higher the sample rate, the less steep the filter needs to be. This is one of the reasons why conventional high-sample-rate digital audio sounds better than standard-rate—the filters are less injurious.
If Meridian were forced to characterize the quality of a digital audio system with a single metric, it would be how much temporal blur the system adds, measured in microseconds or milliseconds. Fig.1 is a chart showing the amount of temporal blur for various audio formats. MQA (shown as the red line) reduces temporal blur to about 10µs, roughly 30 times lower than the temporal blur of regular 192kHz/24-bit PCM, and much lower than that of CD.
Fig.2 shows the respective impulse responses of typical 192/24 and MQA. The narrower the impulse, the less temporal blur. MQA achieves this with a new approach coupling sampling with reconstruction, and, surprisingly, recognition that the very aliasing that the filter exists to prevent can, under certain managed conditions, be less harmful than the filter itself. Another innovation is selecting the filter’s characteristics based on the song or piece of music. By contrast, conventional digital filters tend to be fixed for “worst-case scenario” signals, as well as exemplifying the view that any aliasing is unacceptable.