I want to make an android application that allow user change the voice during phone call. For example: You are a man, you can change the voice to a woman or robot when talking over phone. It is like a funny prank.
I work around android's API and google for some days but still have no idea. Some one told is impossible but I see some app on google play can do:
https://play.google.com/store/apps/details?id=com.gridmob.android.funnycall
So I think there are some ways to do that.
I think about recording and play back by using AudioTracker but I have 2more problem:
1. I cannot mute the voice from phone call, so the phone only play my sound after processing
2. record and process will make a long delay (slow-realtime)
Can any one share some solution for this?
The app you linked isn't changing voices on the phone: it uses SIP (or similar) to place a call through the authors' servers and the voice changing happens there. That's why you only get a small number of free minutes of use before you have to pay them.
Yes it uses a sip server to do this process. The reason you cannot actually create an app that does this on the phone is because of two things. The first thing being, sound processing for the phone is locked. You can't unlock this because its strictly engineered through hardware not software. A pc can do this because it uses a standard sound card in which software can modify its frequencies. The second thing is phone manufactures are required to design their phones in a standard format. There are laws that force these companies to make it impossible to do any voice morphing. It is against the law to impersonate someone you are not, over any telephone network.
Hard way
You get the input voice, you use voice recognition to detect the words, then you use speech-to-text with your desired voice as output.
Less hard way
Sound processing: Changing frequencies, amplitude etc.
Related
For the Dutch movie "App" (http://www.imdb.com/title/tt2536436), a second screen app was developed. This app synchronizes with the audio of the movie to give extra details about some scenes and some other movie fragments from other angles. It seems like it is synchronized with the audio of the movie.
For a school project, a similar app has to be developed, so we want to achieve the same result. Does anybody know of any way to synchronize app content with an external audio source? We know we have to account for environmental audio to be filtered out, but have no idea where to start. It seems that MPEG2-TS has some kind of time coding via a protocol called SMPTE, but we don't know how to "listen" to this time coding in our android app.
Does anybody have any idea? Any external libraries to be used?
Here's an article briefly explaining some Automated Content Recognition (ACR) techniques:
Second screen apps use the microphone on your phone or tablet to listen to the TV and identify the channel, show or ad you are watching, and the precise location within it, based on one of two techniques:
Watermarking
Audio watermarking requires a series of inaudible “watermarks” to be encoded into the broadcast TV signal, normally using a hardware encoder in the playout suite or OB truck. Watermarks can be repeated regularly throughout the broadcast, providing a timecode, or used to trigger specific events such as questions or voting windows. The second screen app uses the device’s microphone to listen for each watermark, and decode the “payload” which uniquely identifies the channel and timecode or event.
Fingerprinting
Audio fingerprinting does not require the broadcast content to be modified. Instead, the TV content is analyzed before it is broadcast (or sometimes while it is being broadcast) and broken up into a sequence of “fingerprints” which are as unique as their name suggests. The second screen app uses an API to record a short segment of audio, and generates its own fingerprint, which is then compared to the “target” set of fingerprints, and if a match is found, the channel, show and timecode are identified.
Thank you for the response. We used watermarking techniques for solving this issue; we added in high-frequency audio watermarks which were detected by the smartphone using FFT analysis of the heard audio. When we detected the TV show that was being watched, we fetched the information of the show over the internet.
I am an Android developer who is living with hearing impairment and I am currently exploring the option of making a speech to text app with Speech Recognizer API in Android. Closed-captioning telephones and Innocaption are not available in in my home country. Potential applications might be like captioning during telephone calls.
https://developer.android.com/reference/android/speech/SpeechRecognizer.html
The API is meant for capturing voice commands, not for real-time live transcribing. I am even able to implement it as a service but I constantly need to restart it after it has delivered a result or a partial result, which is not feasible in a conversational setting (words get lost while the service is restarting).
Do note that I don't need a 100% accuracy for this app. Many hearing impaired people find it helpful to have some context of the conversation to help them along. So I don't actually need comments about how this is not going to be accurate.
Is there a way to implement Speech Recognizer in a continuous mode? I can create a textview that constantly updates itself when new text is returned from the service. If this API is not what I should be looking at, is there any recommendation? I tested CMUSphinx but find that it is too dependent on blocks of phrases/sentences that it is not likely to work for the kind of application I have in mind.
I am a deaf software developer, so I can chime in. I've been monitoring the state of art of Speech-To-Text APIs, and the APIs have now become "good enough" to provide operatorless relay/captioning services for CERTAIN kinds of phone conversations with people using telephone in quiet settings. For example, I get 98% transcription accuracy with my spouse's voice with the Apple Siri realtime transcription (iOS 8).
I was able to jerryrig phone captioning by routing the sound out of one phone, to a 2nd iPhone that I press the microphone button (popup keyboard), and successfully captioned a telephone conversation with ~95% accuracy at 250 words per minute (faster than Sprint Captioned Telephone and Hamilton Captioned Telephone), at least until the 1 minute cutoff time.
Thusly, I declare computer-based voice recognition practical for phone calls with family members (of the type you call frequently in quiet environments), where you can at least coach them to move to a quiet place to allow captioning to work properly (with >95% accuracy). Since iOS 8 got released, we REALLY need this, so we don't need to rely on rely operators or captioning telephone. Sprint Captioned telephone lags badly during fast speech, while Apple Siri keeps up, so I can conduct more natural telephone conversations with my jerryrigged two-iOS-device Apple Siri "realtime Captioned Telephone" setup.
Some cellphones transmit audio in a higher-def manner, so it works well between two iPhones (iPhone speaker piped into another iPhone's Siri running in iOS8 continuous mode). That's assuming you're on G.722.2 (AMR-WB), like when running two iPhones on the same carrier that supports the high-def audio telephony standard. It works perfectly when piped through Siri -- roughly as good as doing it in front of the phone, for the same human voice (assuming the other end is speaking into the phone in a quiet environment).
Google and Apple needs to open up their speech-to-text APIs to assistive applications, pronto, because operatorless telephone transcription is finally now practical, at least when calling family members (good voices & coached to be in a quiet environment when receiving call). The continuous recognition time limit needs to also be removed during this situation, too.
Google is not going to work with telephone quality audio anyway, you need to work on captioning service using CMUSphinx yourself.
You probably didn't configure CMUSphinx properly, it should be ok for large vocabulary transcription, the only thing you should care about is to use telephony 8khz model, not wideband model and generic language model.
For the best accuracy it's probably worth to move processing on the server, you can setup the PBX to make the calls and transcribe audio there instead of hoping to do something on a limited device.
It is true that the SpeechRecognizer API documentation claims that
The implementation of this API is likely to stream audio to remote
servers to perform speech recognition. As such this API is not
intended to be used for continuous recognition, which would consume a
significant amount of battery and bandwidth.
This bit of text was added a year ago (https://android.googlesource.com/platform/frameworks/base/+/2921cee3048f7e64ba6645d50a1c1705ef9658f8). However, no changes were made to the API at the time, i.e. the API remained the same. Also, I don't really see anything specific to networking and battery drain in the API documentation. So, go ahead and implement a recognizer (maybe based on CMUSphinx) and make it accessible via this API.
I am working on one voice messaging application, I need to compare two voice like,
Register with app by record your voice
Sent voice message to
another user by record voice, but first need to compare this voice
to recorded voice in profile.
Its for security purpose and need to know recorded message is from specific user or not.
I tried :
Compare two sound in Android
http://www.dreamincode.net/forums/topic/274280-using-fft-to-compare-two-audio-files-and-then-realtime-comparison/
But not getting idea about voice Comparison.
Please share if anybody know about the same. Didn't find any sample to do this.
Since you indicated it's for security purpose, I'd like to first share a few things on voice biometry :-)
The problem with authenticating someone is that you'd need to be sure he was actually there saying the things that were recorded... and that's a whole different level of complexity than merely comparing voice characteristics.
Algorithms extracting voice features from a sample and later calculating the distance between a new sample and the first one can easily be fooled by a recording made up by an attacker.
Since in your case there's a human recipient, creating a message made up of chopped words or sentences from random conversations is actually quite difficult and time consuming. But not completely impossible...
There are very good sounding softwares created for the music industry that will e.g. take some voice audio input and make it sound (intonation and time wise) like a second audio sample (a guide, made by the fraudster). Vocalign Pro by SynchroArts does this to help get perfect backing vocal tracks. You could further tweak the audio by hand using other voice editing softwares and achieve an acceptable level of quality that wouldn't be immediately detected by the recipient.
Depending on what the attacker wants your user to say, the process complexity could range from an hour to a day provided he has all the recording material he wants...
To fight against this type of attack, you need to detect the audio sample has been edited. The digital edition will leave unnatural traces. E.g. in the background noise surrounding the voice.
AFAICT, only the best commercial softwares achieve this level security check, but I can't tell how far they go in the detection of such edits.
From a pure security perspective, you'd also need to be sure the device was not compromised. So these voice verification check should happen server side and not on the phone itself.
Please note these are general considerations and it all depends on what sort of security measures you actually need for your use case. My car alarm is certainly not unbreakable but it helps raising the bar so fewer attackers could potentially steal it...
Another thing to consider is that biometry is by definition a statistical process and it will yield a certain percentage of false positives and false negatives. By changing the acceptance threshold, you'll be able to lower one of them at the cost of raising the other.
Selecting an appropriate threshold will require you to have a fair amount of test data. Say 1 minute recording of at least 200 speakers to start getting a picture.
One more thing I think you'll need to consider is the inherent variability of the human voice. People may be sick which in some cases might render the voice unrecognizable. Also the emotional state might play a role: sadness or anger will yield different sounding voices...
And last but not least, the surrounding noise might pose a problem. Say the user enrolled while at home and later records a message while on the go in a busy city environment, the system might have troubles making sure it's actually the same person speaking. The signal to noise ratio is definitely going to be one of your main issues. Small tip: depending on the distance of the microphone to the mouth, the ratio will be quite different. You'll get way better result when the user puts the phone close to its face like in a regular phone conversation than when the user looks at the screen while recording the message.
Voice variability and signal to noise ratio are probably the main reasons behind false negative results.
Hopefully, you now have a better understanding of the challenges awaiting you and I can start sharing some pointers for open source and commercial libraries.
AFAIK, there are no open source libraries that includes fraudster detection...
You may want to check Nuance Communication for state-of-the-art. There are plenty other vendors, just check with Google, I only mentioned Nuance because of it's reputation.
There is an OSS library called Alize (written in C++, under LGPL license) which uses an algorithm called MFCC (Mel Frequency Cepstrum Coefficients). MFCC is known to bring excellent results. Expect a steep learning curve as this software is aimed at researchers willing to improve the state-of-the-art on this topic and the vocabulary used is very specific.
I wrote an OSS library named Recognito (Java, Apache 2.0) aimed at regular developers so you should be able to test it in a matter of minutes. The lib is very young and I first focused on it's API before improving the algorithms. The algorithm I use for the moment is called Linear Predictive Coding (LPC) and is known to bring good results (and I do have good results, provided recordings yield the same level of quality :-)). I'm currently in the process of releasing a new version including a likelihood coefficient in the match results. MFCC implementation is on the road map.
There is plenty of javadoc and the code should be very straightforward...
https://github.com/amaurycrickx/recognito
Recognito has a dependency on javax.sound packages for audio file handling. You may want to check this post for what it takes to use it in Android: Voice matching in android
Given many people need something for android, I'll do something about it in the near future instead of saying how one should modify the lib :-)
HTH
Does anyone have any idea how to create an application on an android as a microphone? Like speaking into the android device and it will amplify the voice out?
Yes , I agree .
However , I have to do this as this is my final year project assigned by my supervisor . So i have to do it by hook or by crook . ):
I have already created the application to amplify the voice out from my android device when i speak to it. But there's echo , very high frequency and sensitive to the background .
Do you guys have any solution to this ?
You really mean a megaphone, as in a self-contained voice amplification device.
Sure, technically it's possible, but there are several reasons to not bother. Most importantly, the amplifiers and speakers on handheld devices cannot match the volume you can already achieve with your voice. Also, you would have to work out the feedback challenges - definitely solvable (phase shifts, minor delays, etc.) but effort nonetheless.
Bottom line: I don't think it's worth doing because even if you make it work, someone standing next to you will be able to shout louder than your handheld device can amplify your voice. Not trying to be negative here, just realistic.
I want to connect speaker output to mic input on Android phone without using any physical cable. Such routing should be transparent to all apps. With this function I can design a bunch of interesting apps, e.g., measuring round-trip delay of a voip call, automatically replying to an incoming call etc.
Some similar questions can be found here:
Background music for call
Outputting audio stream into microphone
It seems that there is no solution on Android right now, but the tool JACK running on Linux is the closet one. Can someone provide me some scripts or codes to create a JACK client that realizes this (speaker -> mic) on Linux? And how hard would it be to port JACK server and client to Android?
Thanks!
Getting Jack on Linux to connect the mic to the speakers:
Start Jack with QJackCtl. This is not as easy as it sounds:
Start QJackCtl.
Click the "Setup..." button.
Choose "alsa" from the "Driver:" dropdown.
Choose your soundcard/soundcards that you want to work with from the "Input Device" and "Output Device" dropdowns. Note that if you have a relatively capable soundcard, there may be a number of possible devices for it (my Audigy 2 ZS has four). Choosing the correct one is beyond the scope of this tutorial.
Check the latency in the bottom-right corner. Realize that Jack itself will take some time to process the sound. This gives you information about that (and I'm sorry, but I don't know precisely what it means). Running Jack is often a tradeoff between getting a super low latency and avoiding "xruns", which are where Jack has to give up on processing a section of audio. Xruns often result in a "click" sound.
Click "OK."
Click the "Connect" button.
This opens the "Connections" panel. This is basically a place that you can plug different Jack applications into one another so they process audio from each other.
Choose the input ("system" on the left) and the output ("system" on the right) and click "Connect". You may need to look through the various things listed under "system" on one or both sides to find the thing that's actually capturing from your mic/outputting to your speakers, in which case . Again, this is beyond the scope of this tutorial; Google is your friend, as is trial and error.
Enjoy the noise of feedback when you bring the microphone near the speakers.
As far as porting Jack to Android goes, you might take a look at this, which basically seems to indicate that Android isn't able to handle the low-latency requirements of Jack (which might also cause you problems in trying to do latency measurements). You could also look at this and this, which might offer an alternative way to do what you're trying to do (I'm not sure where it stands regarding the latency issues, also, I've never used either PD or libpd myself).