What is the translation of " SPECTROGRAM " in Chinese?

• 频谱图

Examples of using Spectrogram in English and their translations into Chinese

This test produced a very clear line of the speed in the spectrogram.
这个测试在频谱图中产生了一条非常清晰的速度线。

The spectrograms are then stored as large matrices of thousands of values.
然后，光谱图被存储为数千个值的大矩阵。

These successive frames are then concatenated into a matrix to form the spectrogram.
然后将这些连续的帧排列成一个矩阵，就形成了这个声谱。

Subsequently, the spectrograms are stored as huge matrices of thousands of values.
然后，光谱图被存储为数千个值的大矩阵。

This array of values can then be treated like a single-channel image, and is known as a spectrogram.
然后，该值数组可被视为单通道图像，称为声谱图。

Spectrograms are visual representations of frequency-based features of audio data.
频谱图是音频数据基于频率特征的视觉表示。

The second is a vocoder that converts those spectrograms into a continuous audio signal.
第二种是声码器，它将这些频谱图转换成连续的音频信号。

A spectrogram is cool because you can actually see musical notes and other pitch patterns in audio data.
频谱图很酷，因为你可以在音频数据中实实在在地看到音符和其他音高模式。

Librosa is a free audio-analysis Python library that can produce spectrograms using CPU.
Librosa是一个免费的音频分析python库，可以使用CPU生成时频谱。

Simultaneously, trace data, spectrograms and other measurements can be viewed inside the browser window.
同时，可通过浏览器窗口查看跟踪数据、频谱和其他测量结果。

If you're targeting general sounds rather than speech you may find you can skip this step and operate directly on the spectrograms.
如果您的目标是一般声音而不是语音，您可以跳过此步骤并直接在声谱图上进行操作。

To simplify the analysis process, the spectrogram was generated with the automatic conversion from frequency to speed.
为了简化分析过程，通过自动转换频率到速度，生成了频谱图。

Spectrograms from the feature extractor need to be converted into an Array3D, which can be fed as input for the model.
来自特征提取器的声谱图需要被转换成Array3D，其可以作为模型的输入。

Chelsea Sanker, a phonetician at Brown University, looked at the spectrogram above to help me figure out what was going on.
布朗大学的电话专家切尔西·桑克尔看着上面的声谱图，帮我弄清楚是怎么回事。

In a spectrogram it appears as horizontal lines, and early versions of the model would confuse it with humpback calls.
在光谱图中，它以水平线的形式出现，早期版本的模型会把它与座头鲸的叫声混淆。

If you are developing in TensorFlow and want to do spectrogram computation on the GPU, that is also possible.
如果你正在使用TensorFlow进行开发并希望在GPU上进行频谱图计算，那幺这也是可以的（WEB.

When creating a spectrogram, you can select frequencies to be used, and that will depend on the type of audio data that you have.
创建频谱图时，你可以选择要使用的频率，这取决于你的音频数据类型。

Subsequently, it extracts so-called amplitude modulation(AM) spectrogram features, which are essentially sound-specific characteristics.
随后，它提取所谓的调幅（AM）频谱图特征，这些特征本质上是特定于声音的特征。

Linguists can study these sound waves and separate them out into their component frequencies, and display them in something called a spectrogram.
语言学家可以研究这些声波并将它们分离成它们的成分频率，并在一种叫做谱图的东西中显示它们。

Daniel feeds those spectrograms into the TensorFlow model to teach it what both blue and fin whale calls look like.
Daniel把这些声谱图输入到TensorFlow模型中，训练它识别蓝鲸和长须鲸的叫声。

In testing on previously unseen subjects, the model similarly condenses all spectrogram segments into a reduced set of features.
在对先前看不见的对象进行测试时，该模型类似地将所有频谱图片段压缩成一组减少的特征。

This spectrogram and video show a changing pattern of radio waves from Saturn known as Saturn Kilometric Radiation, as detected by NASA's Cassini spacecraft.
这个频谱图和视频显示了来自土星的无线电波的变化模式，称为土星千米辐射，正如美国宇航局的卡西尼号航天器所探测到的。

The computer then uses common transcribed spectrograms to"train" the system to better recognize this less common type of speech.
之后，计算机使用通用的转录谱图“训练”系统，以便更好地识别你这类不太常见的语音类型。

So, an autoencoder- a neural network optimized to generate efficient data encodings from large amounts of data- first compresses the spectrogram into an encoding of 30 values.
因此，自动编码器-一种优化用于从大量数据生成有效数据编码的神经网络-首先将频谱图压缩为30个值的编码。

In doing so, it is forced to learn the compressed representation of each spectrogram segment input over the whole time-series data of each subject.
在这样做时，它被迫学习每个主题的整个时间序列数据上每个谱图段输入的压缩表示。

In testing on previously unseen subjects, the model similarly condenses all spectrogram segments into a reduced set of features.
在对以前从未见过的物体进行测试时，该模型同样将所有光谱图片段浓缩成一组简化的特征。

End-to-end deep learning language or dialect identification systems operate on the spectrogram or other acoustic feature and directly generate identification scores for each class.
端到端深度学习语言或方言识别系统在频谱图或其他声学特征中操作，并直接生成每个类别的识别分数。