We just released Opus 1.1-alpha, which includes more than one year of development compared to the 1.0.x branch. There are quality improvements, optimizations, bug fixes, as well as an experimental speech/music detector for mode decisions. That being said, it's still an alpha release, which means it can also do stupid things sometimes. If you come across any of those, please let us know so we can fix it. You can send an email to the mailing list, or join us on IRC in #opus on irc.freenode.net. The main reason for releasing this alpha is to get feedback about what works and what does not.
Most of the quality improvements come from the unconstrained variable bitrate (VBR). In the 1.0.x encoder VBR always attempts to meet its target bitrate. The new VBR code is free to deviate from its target depending on how difficult the file is to encode. In addition to boosting the rate of transients like 1.0.x goes, the new encoder also boosts the rate of tonal signals which are harder to code for Opus. On the other hand, for signals with a narrow stereo image, Opus can reduce the bitrate. What this means in the end is that some files may significantly deviate from the target. For example, someone encoding his music collection at 64 kb/s (nominal) may find that some files end up using as low as 48 kb/s, while others may use up to about 96 kb/s. However, for a large enough collection, the average should be fairly close to the target.
There are a few more ways in which the alpha improves quality. The dynamic allocation code was improved and made more aggressive, the transient detector was once again rewritten, and so was the tf analysis code. A simple thing that improves quality of some files is the new DC rejection (3-Hz high-pass) filter. DC is not supposed to be present in audio signals, but it sometimes is and harms quality. At last, there are many minor improvements for speech quality (both on the SILK side and on the CELT side), including changes to the pitch estimator.
Another big feature is automatic detection of speech and music. This is useful for selecting the optimal encoding mode between SILK-only/hybrid and CELT-only. Unlike what some people think, it's not as simple as encoding all music with CELT and all speech with SILK. It also depends on the bitrate (at very low rate, we'll use SILK for music and at high rate, we'll use CELT for speech). Automatic detection isn't easy, but doing so in real-time (with no look-ahead) is even harder. Because of that the detector tends to take 1-2 seconds before reacting to transitions and will sometimes make bad decisions. We'd be interested in knowing about any screw ups of the algorithm.
The new encoder can also detect the bandwidth of the input signal. This is useful to avoid wasting bits encoding frequencies that aren't present in the signal. While easier than speech/music detection, bandwidth detection isn't as easy as it sounds because of aliasing, quantization and dithering. The current algorithm should do a reasonable job, but again we'd be interested in knowing about any failure.