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The maximum number of channels available is 6 for narrowband audio, or 8 for wideband audio.
3. Usage of multi-core available Opus can take advantage of multiple . This helps Opus achieve fast performance, which is important for interactive .
To achieve this, Opus supports dynamic bit allocation (dynamic bitrate support) .
Opus is designed to handle a wide range of interactive audio applications,. Examples include music reproduction, streaming media, remote control, car navigation, and communication of .
Many of these applications have received much popularity with the growth of mobile phone users and the advancement of mobile technology.
[page 8] RFC 6716 Interactive Audio Codec September 2012 In the following, we are going to discuss these applications in more detail.
A typical use case for media streaming is as follows:. Video displays will flash a few seconds or minutes a second .
Windows Virtual Delivery Agent (VDA)
Supported Audio Formats. Currently supported by VDA is Opus, and is required in order to download the Media Source Control (MSS).
Supported Audio Formats. Currently supported by VDA is Opus, and is required in order to download the Media Source Control (MSS).
What Audio Formats Are Supported?
Traditionally, Bluetooth Low Energy devices, as well as devices with no output or only a speaker, can use the Opus codec to transfer audio between devices when their physical connections are not present.
Rate Control and Voice/Music Mode
Rate control is a means of adjusting the bitrate of an audio stream to a desired level at a given time . Applications that don’t use rate control are typically not sufficiently robust or standardized for the purpose of royalty-free distribution .
VoIP Transfer
VoIP transfer applications are typically implemented on top of a packet-switched network (e.g., Internet), which should provide end-to-end service.
Examples include push-to-talk, audio-only Internet telephony, and group chat .
Independence from Transport Media
Because the operation of interactive applications can vary greatly, they typically have a small working memory (e.g., about 4 KB).
To allow for more state-rich applications while

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How can I calculate the average of a Spark dataframe based on the values of two specific columns?

Given a data frame in pyspark, how can I calculate the average of values from one column where the values from a second column are a given value? I.e.
+—-+—+—-+—+—+
|Col1|Col2| Col3| Col4|
+—-+—+—-+—+—+
| 0| 2| 1| 1| 4|
+—-+—+—-+—+—+
| 0| 4| 3| 1| 3|
+—-+—+—-+—+—+

I’m using Spark 1.6.2.

A:

You could use agg() on a Window. Below I’m simply selecting the average of col3 by avg(col3).
from pyspark.sql.window import Window

w = Window.partitionBy(df.col1).orderBy(df.col1, df.col2)
df.withColumn(‘avgCol3’, avg(df.col3)).where(‘avgCol3 > 2’) \
.drop(df.col1, df.col2) \
.orderBy(
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