We are having a Skill built for us which plays podcasts and audio snippets of videos. We currently serve all of this content through other traditional platforms like native mobile apps and a website.
One problem we have is that the volume of all this audio content is much lower than the rest of the sounds outputted from the Alexa device. We don't notice any similar discrepancies on the other aforementioned platforms, and the developers building the Skill say that there's no API which allows you to boost or manipulate the output volume (not the system volume).
Has anyone else had experiences with this sort of issue? We are reluctant to pump up the volume of our source files as it will affect all the other places they are listened to.
Short answer - yes it's tricky and you're not alone.
As detailed in this BBC report* on designing a voice application - "All of our experts have experienced problems in audio levels when mixing and mastering audio for smart speakers."
Official guidence from Amazon is:
Program loudness for Alexa should average -14 dB LUFS/LKFS
The true-peak value should not exceed -2 dBFS
It then goes so far as to say that,
Your skill may be rejected if program loudness:
Is lower than -19 dB LUFS
Is higher than -9dB LUFS
However, this mainly seems to apply to audio played either as part of a Flash Briefing or using the AudioPlayer Interface, and you may get away with deviating from this if using it as part of SSML output.
That said, in practice developers tend to bump the levels up on audio until it sounds good. Sticking strictly to the recommendations usually renders the volume way too low.
So, if you didn't want to increase the loudness of clips as it'd affect other platforms, your best options might be to create an Alexa-version of the audio - mixed slightly louder. Though, I recognize that this would be tedious.
*In the interest of transparency, I wanted to declare that I fed into this report.
I've made my app running on the Google Glass, but it's a little slow in real time. Is there a way to connect my Android phone with the Glass for data communication, so that the phone can take care of the calculation, and the Glass only show the result? The Glass can tether with Android phone by bluetooth, so it should be able to transmit data via it?
Don't know if it's possible to run my app on cloud server and send the result back to my Glass, but guessing that would be slow as well.
Any suggestion is more than welcome! thanks.
Yes, you can connect your Android phone with Glass for data communication, to receive internet content for example. This can be accomplished using Glass to WiFi (but you need the phone to set this up the first time), or Glass to phone to Bluetooth if your phone supports bluetooth tethering, which is often a carrier option.
If you are a Glass explorer this should have been explained when you picked up your Glass, but you can contact the Glass Guides for more information, if you are a Glass Explorer you will have this contact information. I have found them to be extremely helpful and fun to work with on usability questions. It doesn't hurt that if you visit them physically they ply you with treats and drinks.
If you are asking if you can open a socket directly between phone and Glass, that is not supported functionality, but you can request it. It might be possible when the GDK is made public, but there is no timeline for that.
If you wanted to do calculations on a phone and pass them to glass they would have to go through the cloud, as described here. Check out the section titled "How developers interact with Glass" and the accompanying graphic. I find it to be fast (sub one second with good connectivity), but that is subjective, your speed needs are not well defined in your question. A consideration is that every round trip of data will count against your API console daily limit, which is 1000 for most everyone. There is also a 10 request/second limitation.
Last note - there are unsupported ways of talking directly between Glass and a phone for a device you have direct access to, but this is not supported, and could not be used by other Glass users very easily. The techniques to accomplish this are alluded to in the Google I/O 2013 session: Voiding Your Warranty: Hacking Glass.
This forum isn't an appropriate one to discuss this, if you were to contact me directly somehow I could give you some pointers in the right direction, but I don't advise this route at all.
I am developing a number of different mobile applications for a number of different mobile devices and I want to quickly test in a local development environment. I was wondering if it is possible (with some sort of hardware) to set up a local desktop CDMA / GSM base station for testing devices over a local personal cellular network. The range does not have to be very far. The alternative is purchasing a SIM card and plans for various carriers but not all carriers/network types are available in our area.
I'm sure I had seen some sort of desktop device that would let you setup local networks for development/testing purposes but can't seem to find it.
Thanks.
About the least expensive such device I know is the Agilent 8960. It's difficult to give an accurate price, since it depends on the options you choose, but you are likely to need to drop about $30,000 or more for a new one.
GSM, GPRS, EDGE, WCDMA, HSPA, CDMA 2000, 1x, EV-DO are all supported, although a box with all of those options in it will be well over the figure I quoted above!
The device has been around for a while though, so you may find something on eBay or via surplus sales and the like.
The upside is that it gives you an enormous amount of control over the cellular environment, and will let you do repeatable throughput tests (something that is really impossible on 'live' networks unless you use statistical techniques and many, many test runs) but the obvious downside is the price!
In a standard network setup you would need an antenna, a BTS, a BSC, a MSC, a GGSN and a SGSN to do data traffic, all horribly expensive and requiring expert knowledge to get the stuff up and running.
If you are interested in experiments then try OpenBSC altough it might be difficult to find BTS hardware.
If you want to buy actual products then have a look at IPaccess. They offer picocell hardware. I am not sure though if their BSC can work without an MSC and SGSN. But still expect a 5-digit price. Tecore also might be worth a visit.
Test and measurement equipment manufacturers might be an alternative as well. There you should check if you actually can branch out the data traffic into the internet or some test server if you need that.
If you want to do this for a living and not for fun, I would assume that simply buying SIMs plus data plans is the cheapest alternative.
You can roll your own cell network! with http://openbts.org/ but stills you need a development kit(Hardware) which is a little expensive. Or you can try to hack your own phone to use it as a radio which is really difficult but cheap.
The answer to this question may be of interest to you also, depending on what your application does:
Testing Mobile Sites
Essentially there are companies that offer a sort of virtual testing service, allowing you test phones with different location and operator combinations.
I was wondering if you creative minds out there could think of some situations or applications in the web environment where Neural Networks would be suitable or an interesting spin.
Edit: Some great ideas here. I was thinking more web centric. Maybe bot detectors or AI in games.
To name a few:
Any type of recommendation system (whether it's movies, books, or targeted advertisement)
Systems where you want to adapt behaviour to user preferences (spam detection, for example)
Recognition tasks (intrusion detection)
Computer Vision oriented tasks (image classification for search engines and indexers, specific objects detection)
Natural Language Processing tasks (document/article classification, again search engines and the like)
The game located at 20q.net is one of my favorite web-based neural networks. You could adapt this idea to create a learning system that knows how to play a simple game and slowly learns how to beat humans at it. As it plays human opponents, it records data on game situations, the actions taken, and whether or not the NN won the game. Every time it plays, win or lose, it gets a little better. (Note: don't try this with too simple of a game like checkers, an overly simple game can have every possible game/combination of moves pre-computed which defeats the purpose of using the NN).
Any sort of classification system based on multiple criteria might be worth looking at. I have heard of some company developing a NN that looks at employee records and determines which ones are the least satisfied or the most likely to quit.
Neural networks are also good for doing certain types of language processing, including OCR or converting text to speech. Try creating a system that can decipher capchas, either from the graphical representation or the audio representation.
If you screen scrap or accept other sites item sales info for price comparison, NN can be used to flag possible errors in the item description for a human to then eyeball.
Often, as one example, computer hardware descriptions are wrong in what capacity, speed, features that are portrayed. Your NN will learn that generally a Video card should not contain a "Raid 10" string. If there is a trend to add Raid to GPUs then your NN will learn this over time by the eyeball-er accepting an advert to teach the NN this is now a new class of hardware.
This hardware example can be extended to other industries.
Web advertising based on consumer choice prediction
Forecasting of user's Web browsing direction in micro-scale and very short term (current session). This idea is quite similar, a generalisation, to the first one. A user browsing Web could be proposed with suggestions with other potentially interesting websites. The suggestions could be relevance-ranked according to prediction calculated in real-time during user's activity. For instance, a list of proposed links or categories or tags could be displayed in form of a cloud and font size indicates rank score. Each and every click a user makes is an input to the forecasting system, so the forecast is being constantly refined to provide user with as much accurate suggestions, in terms of match against user's interest, as possible.
Ignoring the "Common web problems" angle request but rather "interesting spin" view.
One of the many ways that a NN can be viewed/configured, is as a giant self adjusting, multi-input, multi-output kind of case flow control.
So when you want to offer match ups that are fuzzy, (not to be confused directly with fuzzy logic per se, which is another area of maths/computing) NN may offer a usable alternative.
So to save energy, you offer a lift club site, one-offs or regular trips. People enter where they are, where they want to go and at what time. Sort by city and display in browse control.
Using a NN you could, over time, offer transport owners to transport seekers by watching what owners and seekers link up. As a owner may not live in the same suburb that a seeker resides. The NN learns over time what variances in owners, seekers physical location difference appear to be acceptable. So it can then expand its search area when offering a seekers potential owners.
An idea.
Search! Recognize! Classify! Basically everything search engines do nowadays could benefit from a dose of neural networks and fuzzy logic. This applies in particular to multimedia content (e.g. content-indexing images and videos) since that's where current search technologies are lagging behind.
One thing that always amazes me is that we still don't have any pseudo-intelligent firewalling technology. Something that says "hey his range of urls is making too much requests when they are not supposed to", blocks them, and sends a report to an administrator. That could be done with a neural network.
On the nasty part of things, some virus makers could find lucrative uses to neural networks. Adaptative trojans that "recognized" credit card numbers on a hard drive (instead of looking for certain cookies) or that "learn" how to mask themselves from detectors automatically.
I've been having fun trying to implement a bot based on a neural net for the Diplomacy board game, interacting via DAIDE protocols. It turns out to be extremely tricky, so I've turned to XCS to simplify the problem.
Suppose EBay used neural nets to predict how likely a particular item was to sell; predict what the best day to list items of that type would be, suggest a starting price or "buy it now price"; or grade your description based on how likely it was to attract buyers? All of those could be useful features, if they worked well enough.
Neural net applications are great for representing discrete choices and the whole behavior of how an individual acts (or how groups of individuals act) when mucking around on the web.
Take news reading for instance:
Back in the olden days, you picked up usually one newspaper (a choice), picked a section (a choice), scanned a page and chose an article (a choice), and read the basics or the entire article (another choice).
Now you choose which news site to visit and continue as above, but now you can drop one paper, pick up another, click on ads, change sections, and keep going with few limits.
The whole use of the web and the choices people make based on their demographics, interests, experience, politics, time of day, location, etc. is a very rich area for NN application. This is especially relevant to news organizations, web page design, ad revenue, and may even be an under explored area.
Of course, it's very hard to predict what one person will do, but put 10,000 of them that are the same age, income, gender, time of day, etc. together and you might be able to predict behavior that will lead to better designs. Imagine a newspaper (or even a game) that could be scaled to people's needs based on demographics. An ad man's dream !
How about connecting users to the closest DNS, and making sure there are as few bounces as possible between the request and the destination?
Friend recommendation in social apps (Linkedin,facebook,etc)
Related to my previous question, is there some realistic chance to extract surveillance camera positions out of google streetview pictures by means of computer vision algorithms? I'm no expert in that area. But it should be easier than face detection and the like.
I think you're wrong about it being an easier problem than face recognition (though I suspect you mean face detection).
Consider that faces are of a reasonably regular shape, generally have 2 eyes a nose and a mouth in a specific configuration whilst surveillance cameras from one manufacturer will look different from those of another and look different from different angles.
With faces, if you can't see the person's face you're not interested in it, but in your scenario you're interested in detecting the camera regardless of it's relative position to you.
Whilst not impossible (i.e. humans can do it!) I don't think computer science is quite up to the task just yet.....
This sounds like the class of problem for which Amazon's Mechanical Turk was invented. I don't believe that an image processing or image recognition algorithm is within our current understanding and hardware/software capabilities.
I definitely agree with Rob that extracting the camera locations is going to be more difficult than face detection (or even recognition).
How about a different tack on your question: how to find the location of the camera taking surveillance images.
There are standard (if complicated) photogrammetry techniques to map 2D or 3D coordinates of objects using photographs from multiple cameras or a single camera at multiple angles. What you're looking for would be "reverse photogrammetry" which I haven't seen before, but this interesting legal anecdote suggests it's feasible.