121–130 of 137 posts
Would be a fun experiment to use something like 3 or even 1 sine to get unintelligible speech, but then pair it with subtitles where each syllable of the text is animated synchronized with the speech. (Like the "follow the bouncing ball" song lyric animations.) By pairing the audio with the text, you would almost certainly convince the listener that they can understand it. Edit: typo
Earlier quoted context omitted.
Without the "intelligible" part, this makes me think of what the game Celeste does to give its characters voices without voice acting. They make voice-like synth sounds, different for each character, that are about the length of the text they're saying. It adds prosody and intonation to the text-based dialogue of the game. https://youtu.be/TZpQH8kSWNU?t=2m50s
This is how I imagine an intelligent car would sound if it figured out how to produce speech through the antigravity engine. Edit: oh sweet there's intonation too. Were these all made manually?
Having grown accustom to MP3 artifacts, it's strange to hear artifacts that are natural, but just aren't quite right. More specifically, in the male voice sample: "sold about seventy-seven", I received it as "sold about se th enty-seven".
In the normal codec2 decoding it sounds like "seventy" but muffled and crunchy.
In the wavenet decoding, the voice sounds clearly higher quality and crisp, but the word sounds more like "suthenty". And not because the audio quality makes it ambiguous but it sounds like it's very deliberately pronouncing "suthenty".
It's as if in trying to enhance and crisp up the sound, it corrected in the wrong direction. It sounds like the compressed data that would otherwise code for a muffled and indistinct "seventy", was interpreted by wavenet but "misheard" in a sense. When wavenet reconstructs the speech, it confidently outputs a much clearer/crisper voice, except it locks onto the wrong speech sounds.
With the standard "muffled/crunchy" decoding, a listener can sort of "hear" this uncertainty. The speech sound is "clearly" indistinct, and we're prompted to do our own correction (in our heads), but also knowing it might be wrong. When the machine learning net does this correction for us, we don't get the additional information of how its guess is uncertain.
This is exactly the sort of artifact I'd expect with this kind of system. As soon as I heard the ridiculously good and crisp audio quality of the wavenet decoder, that fidelity just isn't included in the encoding bits, that's impossible. It's a great accomplishment and just impressive, but it has to "make up" some of those details in a sense very similar to image super resolution algorithms.
I'm just thinking we should perhaps be careful to not get into a situation like the children's "telephone" game, if for some reason the speech gets re/de/re/encoded more than once. Which is of course bad practice, but even if it happens by accident, the wavenet will decode into confident and crisp audio, so it may be hard to notice if you don't expect it.
If audio is encoded and decoded a few times, it's possible that the wavenet will in fact amplify misheard speech sounds into radically different speech sounds, syllables or even words, changing the meaning. Kind of like the "deep dreaming" networks. Sounds like a particularly bad idea for encoding audio books, because small flourishes in wording really can matter.
Edit: I just realised that repeated re/de/re-encoding can in fact happen quite easily if this codec is ever implemented and used in real world phone networks. Many networks use different codecs and re-encoding just has to be done if something is to pass through a particular network.
But the whole thing is ridiculously cool regardless :) And I wonder if they can improve on this problem.
The article failed to mention the original reason why Codec2 is invented. In digital amateur radio communication, currently the most widely-used codec is AMBE. But AMBE is a proprietary codec, covered by patents, unhackable - the counter-thesis of amateur radio. Codec2 was born to bring freedom to digital amateur radio communication, and technically even better than AMBE.
Earlier quoted context omitted.
I highly doubt there are any devices that are capable of accessing the modern web, with all its JavaScript bloat, yet cannot decode a simple audio codec. Even when Apple was installing AAC hardware decoders, they were already almost obsolete by modern embedded CPU development (especially the rise of medium-power ARM SoCs). I highly doubt any devices released in the past 5 years have any sort of fixed-function audio d…
Yeah, the last time hardware audio decoders were relevant was like... back in the Nokia N-Gage days. The N-Gage QD removed the MP3 decoder that was present in the original model. And you could install a software player, and it would struggle with bitrates above 128kbps :D Modern phones can decode video in software (sucks for battery life, and framerate/resolution are more limited than with hardware, but it's possible…
I guess it's irrelevant you feel overwhelmed by how long your phone can go on a charge. Plus, low-power/low-CPU requirements are an order of magnitude more critical in devices like smartwatches.
Earlier quoted context omitted.
All the +2k podcasts hosted on Podigee (a podcast hosting company mainly known in German-speaking countries) are distributed in opus. But it is, and probably will always be, a rather niche distribution format. AAC had its moment, but MP3 is alive and kicking. Even Apple acknowledges its importance by adding support for chapter markers in iOS12.
There are a lot of playback issues - VBR MP3 and older OS releases of both iOS and Android, never mind the car players and similar all contribute to the problem. The post-show of this podcast talks about these and other issues in detail - Marco is on both sides of the issue as a podcast producer and podcast app developer: http://atp.fm/episodes/182
Earlier quoted context omitted.
Speech to text is certainly getting better but it makes mistakes. If the transcribed text was sent over the link and then a text to speech spoke at the other end you'd lose one of the great things about codec2 - the voice that comes out is recognisable as it sounds a bit like the person. A few of us have a contact on Sunday mornings here in Eastern Australia and it's amazing how the ear gets used to the sound and it…
Could you elaborate on "a contact"? Are you using Codec2 over radio?
[1] - https://freedv.org/
Let say we have Codec2 with WaveNet, its 3.2Kbps now does similar to may be 16Kbps EVS. ( EVS being the codec used in VoLTE, which is slightly better then even Opus in Speeches. ) What "value" / "uses" does this bring us? It cant be used in podcast because as shown it isn't very good with Music. And many podcast has Music in it. While Codec 2 with WaveNet can have a 2-4x reduction in bitrate. I cant think of a applic…
Maybe not too much for "us" with LTE and 128GB storage on our phones, but in cases of low bandwith (think digital police radio), or when you have low storage availability, that's really awesome.
The authors are not from Cornell. I think the author made this mistake because the paper is posted on arXiv, and that’s what’s it says at the top of every page?
"The man behind it, David Rowe, is an electronic engineer currently living in South Australia. He started the project in September 2009, with the main aim of improving low-cost radio communication for people living in remote areas of the world. With this in mind, he set out to develop a codec that would significantly reduce file sizes and the bandwidth required when streaming." What do you know, it's sort of like Pie…