Does AVRCP 1.3 support album art? Based on list of media attributes, album art is not included in the list. If AVRCP 1.3 do support album art, how do the information pass?
The short answer is no, it does not support album art. As you mentioned, AVRCP 1.3 does not support album art. As you mention, Appendix E: List of media attributes in AVRCP 1.3 is the source. Neiter does the latter versions.
In theory, you could use one of the reserved bytes to define your own album art metadata field. How well this would work practically would depend on a bunch of factors, e.g.
Both the sender and the receiver are able to interpret the new information,
The Bluetooth stacks on both sides allows the actual extention.
And of course, if you extend the protocol, you are not conforming with the standard anymore.
Album Art is supported in AVRCP 1.6 and up. A great many autos are actually getting the cover art via some form of onboard CDDB database like gracenote. This is NOT sent over Bluetooth.
Related
Is BLE's Data Length Extension (DLE) compatible with Bluetooth Mesh?
I've looked at as many places as I could think, but nowhere does it say that is, nor does it say that it is not.
No, as of this writing BLE mesh does not support Data Length Extension. The main reason for this is that BLE mesh technology is advert-based, and for BLE adverts to have ~251 bytes size you need the BT5 feature BLE Advertising Extensions, and this feature is not defined in the mesh specification.
There are however custom implementations of BLE mesh that take advantage of this feature (e.g. Nordic's Instaburst). However, these don't fully comply with the mesh standard and therefore will not work with all device.
References:-
Nordic's Advertiser Extensions (Instaburst)
Mesh Networking Specifications
I hope this helps.
I am currently conducting research on American pop songs using Spotify's audio features (e.g., danceability, tempo, and valence...). But, I couldn't find any documentation that contains details about how they measured the features. I know there's a brief description of the features. But, it doesn't tell about any the exact measurement. Could you let me know where I can find it?
Thanks.
The Echonest was a music data analysis platform acquired by Spotify, and its expertise is being currently used to power up Spotify recommendation tools.
Audio Features API endpoint extracts a more "High Level" analysis from audio and songs, whereas Audio Analysis endpoint extracts more "Low Level" and technical data.
Essentially, "High-level" features are more explicit and make use of clearer semantics -plain english, in order to be easily understood by the layman ("danceability", for instance), but it all comes from Low Level analysis, really.
Here you have some documentation, if you wish to dive deeper into the matter:
http://docs.echonest.com.s3-website-us-east-1.amazonaws.com/_static/AnalyzeDocumentation.pdf
I am looking into persona devices as described in Appendix G of the Redhawk manual.
Is there a detailed "how to" for this anywhere?
In my scenario my 'Programmable Device' would be a Redhawk FEI device that interfaces with a kernel API that controls tuners, fans, gps, buttons and LCD displays. I would like to break this out into three or four persona devices that interface with the main FEI Device.
Thought I'd ask.
If you head to Geon's github and look at the RFNoC_ProgrammableDevice and RFNoC_DefaultPersona, you can get an idea of how these Devices interact with one another. It should be noted that these Devices are still under development. Unfortunately, the manual appendix you mentioned and these examples are really the closest thing to a "how to" there is right now.
That being said, this pattern is generally reserved for FPGAs, with the programmable Device controlling access to the programmable hardware (and FEI functionality, if present) and the persona(s) controlling access to specific bit file capabilities. If you're not interacting with an FPGA, then the pattern will most likely be more trouble than it's worth to obtain modularity.
I have an RFID reader which is ISO 14443A compliant. It is capable of reading Mifare 1k (s50), Mifare 4k (s70), and Mifare Mini (s20) cards. I want to know if the same RFID reader can read the cards which are ISO 15693 compliant. I am new with RFID and I dont know anything about the ISO standards.
Compliancy to ISO14443 does not imply compliancy to ISO15693. However, some reader chips can do both. If you can tell the model name of your reader, or the reader chip inside, it may be possible to tell whether it supports ISO15693.
Check with your reader manufacturer to see if it supports both protocols. Many do; but, you should double check to be sure.
Even if it does support both, it will likely NOT be able to do so simultaneously. Likely, you will have to toggle between the two protocols in order work with both types of RFID tags.
ISO14443 A/B and ISO15693 standard operate on the same frequency 13.56 MHz, and both have about the same read range when reading tags 5 - 20 cm, but from then on the similarities end.
They have different ways to access RFID tags and perform inventory, data read/write and different memory organization.
Because of the similarities some manufacturers provide readers that can handle both types of tags, but the procedure is different due to the different standards (so a software designed to read ISO14443 will not read ISO15693 and vice-versa).
As previous answers you will need to check with your manufacturer to be sure, but if you need a recommendation of reader with which I have worked you can try the IDTronic Desktop EVO HF or IDTronic Desktop EVO LEGIC. From my knowledge it's under 100 $.
Datasheet here:
Desktop EVO Reader Datasheet
the RFID reader ISO 14443A can not read the cards which are ISO 15693.
they are totally two different kind standard.
for rfid reader, i think you could first learn from this rfid news here http://syncotek.com/news/
I am working on a project where biometric system is used to secure the system. We are planning to use human voice to secure the system.
Idea is to allow the person to say some words or sentences and system will store that voice in digital format. Next time person wants to enter the system, he/she has to speak some words which may or may not be different from the words used earlier.
We don't want to match words but want to match voice frequency.
I have read some research papers regarding this system but those papers don't have any implementation details.
So just want to know whether there is any software/API which can convert analog voice into digital format and will also tell us the frequency of voice.
Until now I was working on normal web based applications so I know normal APIs and platforms like Java EE, C#, etc but I don't have any experience about this kind of application.
Please enlighten !!!
http://www.loquendo.com/en/products/speaker-verification/
http://www.nuance.com/for-business/by-solution/contact-center-customer-care/cccc-solutions-services/verifier/index.htm
(two links removed due to reported virus content)
http://www.persay.com/products.asp
This is as good a starting point as any : http://marsyas.info/
It's a open source software framework for audio processing. They've listed a bunch of projects that have used their framework in various ways so you could probably draw inspiration from it.
http://marsyas.info/about/projects. The Telligence project in particular seems the closest to your needs as it it was used to gender classify audio : http://marsyas.info/about/projects#5Teligence
There are two steps on a project like this one I believe:
First step would be to record the voice from an analog input into digital format (let's assume wav-pcm). For this you can use DirectShow API in C#, or standard Wav-In as in this project: http://www.codeproject.com/KB/audio-video/cswavrec.aspx. You may consider compressing your audio files later on, there are many options for this, in Windows you may consider Windows Media Format SDK to avoid licensing issues with other formats.
Second step is to build or use a voice recognition framework, if you want to build a recognition framework you will probably need to define a set of "features" for your sound fragments and select+implement a recognition algorithm. There are many aproaches available for this, IEEE amd ACM.org websties are usually good sources. If you want to use an existing framework you may want to consider Nuance Recognizer (commercial) or http://cmusphinx.sourceforge.net (open source).
Hope this helps.