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Online Resource

Electronic health record (EHR) log data have shown promise in measuring physician time spent on clinical activities, contributing to deeper understanding and further optimization of the clinical environment.

Online Resource

On-scroll animations can add quite the elegant effect to your website. A little animated polish can go a long way towards making your site look well-rounded and complete. With JavaScript and CSS, you can make elements fade, slide, or even …

Chris Mills brushes up his shorthand and shows how the MediaStream Recording API in modern browsers can be used to capture audio directly from the user’s device. Inching ever closer to the capabilities of native software, it truly is an exciting time to be a web developer.

The MediaStream Recording API makes it easy to record audio and/or video streams. When used with MediaDevices.getUserMedia(), it provides an easy way to record media from the user’s input devices and instantly use the result in web apps. This article shows how to use these technologies to create a fun dictaphone app.

A sample application: Web Dictaphone

To demonstrate basic usage of the MediaRecorder API, we have built a web-based dictaphone. It allows you to record snippets of audio and then play them back. It even gives you a visualisation of your device’s sound input, using the Web Audio API. We’ll just concentrate on the recording and playback functionality in this article, for brevity’s sake.

You can see this demo running live, or grab the source code on GitHub. This has pretty good support on modern desktop browsers, but pretty patchy support on mobile browsers currently.

Basic app setup

To grab the media stream we want to capture, we use getUserMedia(). We then use the MediaRecorder API to record the stream, and output each recorded snippet into the source of a generated <audio> element so it can be played back.

We’ll first declare some variables for the record and stop buttons, and the <article> that will contain the generated audio players:

const record = document.querySelector('.record');
const stop = document.querySelector('.stop');
const soundClips = document.querySelector('.sound-clips');

Next, we set up the basic getUserMedia structure:

if (navigator.mediaDevices && navigator.mediaDevices.getUserMedia) {
   console.log('getUserMedia supported.');
   navigator.mediaDevices.getUserMedia (
      // constraints - only audio needed for this app
      {
         audio: true
      })

      // Success callback
      .then(function(stream) {

      })

      // Error callback
      .catch(function(err) {
         console.log('The following `getUserMedia` error occured: ' + err);
      }
   );
} else {
   console.log('getUserMedia not supported on your browser!');
}

The whole thing is wrapped in a test that checks whether getUserMedia is supported before running anything else. Next, we call getUserMedia() and inside it define:

• The constraints: Only audio is to be captured for our dictaphone.
• The success callback: This code is run once the getUserMedia call has been completed successfully.
• The error/failure callback: The code is run if the getUserMedia call fails for whatever reason.

Note: All of the code below is found inside the getUserMedia success callback in the finished version.

Capturing the media stream

Once getUserMedia has created a media stream successfully, you create a new Media Recorder instance with the MediaRecorder() constructor and pass it the stream directly. This is your entry point into using the MediaRecorder API — the stream is now ready to be captured into a <Blob>, in the default encoding format of your browser.

const mediaRecorder = new MediaRecorder(stream);

There are a series of methods available in the MediaRecorder interface that allow you to control recording of the media stream; in Web Dictaphone we just make use of two, and listen to some events. First of all, MediaRecorder.start() is used to start recording the stream once the record button is pressed:

record.onclick = function() {
  mediaRecorder.start();
  console.log(mediaRecorder.state);
  console.log("recorder started");
  record.style.background = "red";
  record.style.color = "black";
}

When the MediaRecorder is recording, the MediaRecorder.state property will return a value of “recording”.

As recording progresses, we need to collect the audio data. We register an event handler to do this using mediaRecorder.ondataavailable:

let chunks = [];

mediaRecorder.ondataavailable = function(e) {
  chunks.push(e.data);
}

Last, we use the MediaRecorder.stop() method to stop the recording when the stop button is pressed, and finalize the Blob ready for use somewhere else in our application.

stop.onclick = function() {
  mediaRecorder.stop();
  console.log(mediaRecorder.state);
  console.log("recorder stopped");
  record.style.background = "";
  record.style.color = "";
}

Note that the recording may also stop naturally if the media stream ends (e.g. if you were grabbing a song track and the track ended, or the user stopped sharing their microphone).

Grabbing and using the blob

When recording has stopped, the state property returns a value of “inactive”, and a stop event is fired. We register an event handler for this using mediaRecorder.onstop, and construct our blob there from all the chunks we have received:

mediaRecorder.onstop = function(e) {
  console.log("recorder stopped");

  const clipName = prompt('Enter a name for your sound clip');

  const clipContainer = document.createElement('article');
  const clipLabel = document.createElement('p');
  const audio = document.createElement('audio');
  const deleteButton = document.createElement('button');

  clipContainer.classList.add('clip');
  audio.setAttribute('controls', '');
  deleteButton.innerHTML = "Delete";
  clipLabel.innerHTML = clipName;

  clipContainer.appendChild(audio);
  clipContainer.appendChild(clipLabel);
  clipContainer.appendChild(deleteButton);
  soundClips.appendChild(clipContainer);

  const blob = new Blob(chunks, { 'type' : 'audio/ogg; codecs=opus' });
  chunks = [];
  const audioURL = window.URL.createObjectURL(blob);
  audio.src = audioURL;

  deleteButton.onclick = function(e) {
    let evtTgt = e.target;
    evtTgt.parentNode.parentNode.removeChild(evtTgt.parentNode);
  }
}

Let’s go through the above code and look at what’s happening.

First, we display a prompt asking the user to name their clip.

Next, we create an HTML structure like the following, inserting it into our clip container, which is an <article> element.

<article class="clip">
  <audio controls></audio>
  <p>_your clip name_</p>
  <button>Delete</button>
</article>

After that, we create a combined Blob out of the recorded audio chunks, and create an object URL pointing to it, using window.URL.createObjectURL(blob). We then set the value of the <audio> element’s src attribute to the object URL, so that when the play button is pressed on the audio player, it will play the Blob.

Finally, we set an onclick handler on the delete button to be a function that deletes the whole clip HTML structure.

So that’s basically it — we have a rough and ready dictaphone. Have fun recording those Christmas jingles! As a reminder, you can find the source code, and see it running live, on the MDN GitHub.

This article is based on Using the MediaStream Recording API by Mozilla Contributors, and is licensed under CC-BY-SA 2.5.

About the author

Chris Mills manages the MDN web docs writers’ team at Mozilla, which involves spreadsheets, meetings, writing docs and demos about open web technologies, and occasional tech talks at conferences and universities. He used to work for Opera and W3C, and enjoys playing heavy metal drums and drinking good beer.

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Episode Summary

Surma is an open web advocate for Google currently working with WebAssembly team. He was invited on the show today to talk about using web workers and how to move work away from the browser’s main thread. His primary platform is bringing multithreading out of the fringes and into the web. 

The panel talks about their past experience with web workers, and many of them found them isolated and difficult to use. Surma believes that web workers should pretty much always be sued because the main thread is an inherently bad place to run your code because it has to do so much. Surma details the differences between web workers, service workers, and worklets and explains what the compositer is. 

The panel discusses what parts should be moved off the main thread and how to move the logic over. Surma notes that the additional cost of using a worker is basically nonexistent, changes almost nothing in your workflow, and takes up only one kilobyte of memory. Therefore, the cost/benefit ratio of using web workers gets very large. They discuss debugging in a web worker and Surma details how debugging is better in web workers. 

Surma wants to see people use workers not because it will make it faster, but because it will make your app more resilient across all devices. Every piece of JavaScript you run could be the straw that breaks the camel’s back. There’s so much to do on the main thread for the browser, especially when it has a weaker processor, that the more stuff you can move away, the better.

The web is tailored for the most powerful phones, but a large portion of the population does not have the most powerful phone available, and moving things over to a web worker will benefit the average phone. Surma talks about his experience using the Nokia 2, on which simple apps run very slow because they are not being frugal with the user’s resources. Moving things to another thread will help phones like this run faster.  

The panel discusses the benefit of using web workers from a business standpoint. The argument is similar to that for accessibility. Though a user may not need that accessibility all the time, they could become in need of it. Making the app run better on low end devices will also increase the target audience, which is helpful is user acquisition is your principle metric for success. 

Surma wants businesses to understand that while this is beneficial for people in countries like India, there is also a very wide spectrum of phone performance in America. He wants to help all of these people and wants companies acknowledge this spectrum and to look at the benefits of using web workers to improve performance.

Panelists

• Charles Max Wood

• Christopher Buecheler

• Aimee Knight

• AJ O’Neal

With special guest: Surma

Sponsors

• Adventures in DevOps

• Sentry use the code “devchat” for 2 months free on Sentry’s small plan

• Adventures in Angular

Links

• Web workers

• Service workers

• Worklets 

• Ecto model

• Babel

• Swoosh

• Comlink

• WhatsApp

Follow DevChatTV on Facebook and Twitter

Picks

Charles Max Wood:

• For Love of Mother-Not

Surma:

• Follow Surma @DasSurma on Twitter and at dassur.ma

• WebAssembly Spec

AJ O’Neal:

• The GameCube Ultimate

• Pikmin for Wii and GameCube

• Super Monkey Ball

Christopher Buecheler

• CinemaSins Sincast podcast

Badger, Michael

Badger, Michael

This study develops and validates a quantitative method to predict the pulmonary to systemic flow ratio in patients with congenital heart disease from chest radiographs using deep learning.

To the Editor We read the recent article by Klauser et al with great interest. While the potential implications of the findings are exciting, we have several concerns. First, the authors do not explicitly state whether electrocardiogram gating was used in their study. This is an important detail because cardiac motion artifact is a source of artifactual coloration with dual-energy computed tomography (DECT), particularly with dual-source scanners given the approximately 80-millisecond temporal difference between the 2 radiography beams. Furthermore, beam hardening artifact from calcified atheromas and partial volume effect, known sources of artifacts in the 2-material decomposition algorithm of DECT, may largely explain the findings. While patients with gout had higher prevalence of coronary calcification (55 of 59 patients [93%]) and cardiovascular monosodium urate (MSU) deposition (51 of 59 patients [86%]) than controls, the authors do not report whether the 4 patients with gout without coronary calcifications exhibited MSU deposition nor the number of controls or cadaveric hearts with coronary calcification. The images from the article show areas of green pixelization occurring adjacent to calcified plaques on grayscale computed tomography images (eg, Figure 2A and D, left anterior descending artery [yellow arrowhead]), which would favor this artifact hypothesis without additional data.

In Reply We appreciate the valuable comments of Becce et al on our article. We applied prospective electrocardiography gating using a thin-slice cardiac protocol to ensure highest spatial resolution with minimal motion artifact. A noncontrast electrocardiography-gated computed tomography (CT) examination with standardized scan parameters was performed using a 128-slice dual-source CT (SOMATOM Definition Flash; Siemens) with a detector collimation of 2 × 64 × 0.6 mm, rotation time of 0.28 seconds, and prospective electrocardiography triggering for heart rates less than 65 beats per minute (diastolic padding, 70% of RR interval) and more than 65 beats per minute (systolic padding, 40% of RR interval). Axial images were reconstructed with 0.75-mm slice width, increment of 0.5, and a medium-smooth convolution kernel (B26f). When motion artifact was present, it was distinguished by visual analysis of an experienced observer and colorized pixels related to motion were excluded.

Shah, Monali, author